Analysis of Attribution Selection Techniques

Analysis of Attribution Selection Techniques ABSTRACT: From a large amount of data, the significant knowledge is discovered by means of applying the techniques and those techniques in the knowledge management process is known as Data mining techniques. For a specific domain, a form of knowledge discovery called data mining is necessary for solving the problems. The classes of unknown data are detected by the technique called classification. Neural networks, rule based, decision trees, Bayesian are the some of the existing methods used for the classification. It is necessary to filter the irrelevant attributes before applying any mining techniques. Embedded, Wrapper and filter techniques are various feature selection techniques used for the filtering. In this paper, we have discussed the attribute selection techniques like Fuzzy Rough SubSets Evaluation and Information Gain Subset Evaluation for selecting the attributes from the large number of attributes and for search methods like BestFirst Search is used for fuzzy rough subset evaluati on and Ranker method is applied for the Information gain evaluation. The decision tree classification techniques like ID3 and J48 algorithm are used for the classification. From this paper, the above techniques are analysed by the Heart Disease Dataset and generate the result and from the result we can conclude which technique will be best for the attribute selection. 1. INTRODUCTION: As the world grows in complexity, overwhelming us with the data it generates, data mining becomes the only hope for elucidating the patterns that underlie it. The manual process of data analysis becomes tedious as size of data grows and the number of dimensions increases, so the process of data analysis needs to be computerised. The term Knowledge Discovery from data (KDD) refers to the automated process of knowledge discovery from databases. The process of KDD is comprised of many steps namely data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge representation. Data mining is a step in the whole process of knowledge discovery which can be explained as a process of extracting or mining knowledge from large amounts of data. Data mining is a form of knowledge discovery essential for solving problems in a specific domain. Data mining can also be explained as the non trivial process that automatically collects the useful hidd en information from the data and is taken on as forms of rule, concept, pattern and so on. The knowledge extracted from data mining, allows the user to find interesting patterns and regularities deeply buried in the data to help in the process of decision making. The data mining tasks can be broadly classified in two categories: descriptive and predictive. Descriptive mining tasks characterize the general properties of the data in the database. Predictive mining tasks perform inference on the current data in order to make predictions. According to different goals, the mining task can be mainly divided into four types: class/concept description, association analysis, classification or prediction and clustering analysis. 2. LITERATURE SURVEY: Data available for mining is raw data. Data may be in different formats as it comes from different sources, it may consist of noisy data, irrelevant attributes, missing data etc. Data needs to be pre processed before applying any kind of data mining algorithm which is done using following steps: Data Integration – If the data to be mined comes from several different sources data needs to be integrated which involves removing inconsistencies in names of attributes or attribute value names between data sets of different sources . Data Cleaning –This step may involve detecting and correcting errors in the data, filling in missing values, etc. Discretization –When the data mining algorithm cannot cope with continuous attributes, discretization needs to be applied. This step consists of transforming a continuous attribute into a categorical attribute, taking only a few discrete values. Discretization often improves the comprehensibility of the discovered knowledge. Attribute Selection – not all attributes are relevant so for selecting a subset of attributes relevant for mining, among all original attributes, attribute selection is required. A Decision Tree Classifier consists of a decision tree generated on the basis of instances. The decision tree has two types of nodes: a) the root and the internal nodes, b) the leaf nodes. The root and the internal nodes are associated with attributes, leaf nodes are associated with classes. Basically, each non-leaf node has an outgoing branch for each possible value of the attribute associated with the node. To determine the class for a new instance using a decision tree, beginning with the root, successive internal nodes are visited until a leaf node is reached. At the root node and at each internal node, a test is applied. The outcome of the test determines the branch traversed, and the next node visited. The class for the instance is the class of the final leaf node. 3. FEATURE SELECTION: Many irrelevant attributes may be present in data to be mined. So they need to be removed. Also many mining algorithms don’t perform well with large amounts of features or attributes. Therefore feature selection techniques needs to be applied before any kind of mining algorithm is applied. The main objectives of feature selection are to avoid overfitting and improve model performance and to provide faster and more cost-effective models. The selection of optimal features adds an extra layer of complexity in the modelling as instead of just finding optimal parameters for full set of features, first optimal feature subset is to be found and the model parameters are to be optimised. Attribute selection methods can be broadly divided into filter and wrapper approaches. In the filter approach the attribute selection method is independent of the data mining algorithm to be applied to the selected attributes and assess the relevance of features by looking only at the intrinsic propert ies of the data. In most cases a feature relevance score is calculated, and lowscoring features are removed. The subset of features left after feature removal is presented as input to the classification algorithm. Advantages of filter techniques are that they easily scale to highdimensional datasets are computationally simple and fast, and as the filter approach is independent of the mining algorithm so feature selection needs to be performed only once, and then different classifiers can be evaluated. 4. ROUGH SETS Any set of all indiscernible (similar) objects is called an elementary set. Any union of some elementary sets is referred to as a crisp or precise set otherwise the set is rough (imprecise, vague). Each rough set has boundary-line cases, i.e., objects which cannot be with certainty classified, by employing the available knowledge, as members of the set or its complement. Obviously rough sets, in contrast to precise sets, cannot be characterized in terms of information about their elements. With any rough set a pair of precise sets called the lower and the upper approximation of the rough set is associated. The lower approximation consists of all objects which surely belong to the set and the upper approximation contains all objects which possible belong to the set. The difference between the upper and the lower approximation constitutes the boundary region of the rough set. Rough set approach to data analysis has many important advantages like provides efficient algorithms for find ing hidden patterns in data, identifies relationships that would not be found using statistical methods, allows both qualitative and quantitative data, finds minimal sets of data (data reduction), evaluates significance of data, easy to understand. 5. ID3 DECISION TREE ALGORITHM: From the available data, using the different attribute values gives the dependent variable (target value) of a new sample by the predictive machine-learning called a decision tree. The attributes are denoted by the internal nodes of a decision tree; in the observed samples, the possible values of these attributes is shown by the branches between the nodes, the classification value (final) of the dependent variable is given by the terminal nodes. Here we are using this type of decision tree for large dataset of telecommunication industry. In the data set, the dependent variable is the attribute that have to be predicted, the values of all other attributes decides the dependent variable value and it is depends on it. The independent variable is the attribute, which predicts the values of the dependent variables. The simple algorithm is followed by this J48 Decision tree classifier. In the available data set using the attribute value, the decision tree is constructed for assort a new item. It describes the attribute that separates the various instances most clearly, whenever it finds a set of items (training set). The highest information gain is given by classifying the instances and the information about the data instances are represent by this feature. We can allot or predict the target value of the new instance by assuring all the respective attributes and their values. 6. J48 DECISION TREE TECHNIQUE: J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. C4.5 is a program that creates a decision tree based on a set of labeled input data. This algorithm was developed by Ross Quinlan. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier (†C4.5 (J48)†. 7. IMPLEMENTATION MODEL: WEKA is a collection of machine learning algorithms for Data Mining tasks. It contains tools for data preprocessing, classification, regression, clustering, association rules, and visualization. For our purpose the classification tools were used. There was no preprocessing of the data. WEKA has four different modes to work in. Simple CLI; provides a simple command-line interface that allows direct execution of WEKA commands. Explorer; an environment for exploring data with WEKA. Experimenter; an environment for performing experiments and conduction of statistical tests between learning schemes. Knowledge Flow; presents a â€Å"data-flow† inspired interface to WEKA. The user can select WEKA components from a tool bar, place them on a layout canvas and connect them together in order to form a â€Å"knowledge flow† for processing and analyzing data. For most of the tests, which will be explained in more detail later, the explorer mode of WEKA is used. But because of the size of some data sets, there was not enough memory to run all the tests this way. Therefore the tests for the larger data sets were executed in the simple CLI mode to save working memory. 8. IMPLEMENTATION RESULT: The attributes that are selected by the Fuzzy Rough Subset Evaluation using Best First Search method and Information Gain Subset Evaluation using Ranker Method is as follows: 8.1 Fuzzy Rough Subset Using Best First Search Method === Attribute Selection on all input data === Search Method: Best first. Start set: no attributes Search direction: forward Stale search after 5 node expansions Total number of subsets evaluated: 90 Merit of best subset found: 1 Attribute Subset Evaluator (supervised, Class (nominal): 14 class): Fuzzy rough feature selection Method: Weak gamma Similarity measure: max(min( (a(y)-(a(x)-sigma_a)) / (a(x)-(a(x)-sigma_a)),((a(x)+sigma_a)-a(y)) / ((a(x)+sigma_a)-a(x)) , 0). Decision similarity: Equivalence Implicator: Lukasiewicz T-Norm: Lukasiewicz Relation composition: Lukasiewicz (S-Norm: Lukasiewicz) Dataset consistency: 1.0 Selected attributes: 1,3,4,5,8,10,12 : 7 0 2 3 4 7 9 11 8.2 Info Gain Subset Evaluation Using Ranker Search Method: === Attribute Selection on all input data === Search Method: Attribute ranking. Attribute Evaluator (supervised, Class (nominal): 14 class): Information Gain Ranking Filter Ranked attributes: 0.208556 13 12 0.192202 3 2 0.175278 12 11 0.129915 9 8 0.12028 8 7 0.119648 10 9 0.111153 11 10 0.066896 2 1 0.056726 1 0 0.024152 7 6 0.000193 6 5 0 4 3 0 5 4 Selected attributes: 13,3,12,9,8,10,11,2,1,7,6,4,5 : 13 8.2 ID3 Classification Result for 14 Attributes: Correctly Classified Instances 266 98.5185 % Incorrectly Classified Instances 4 1.4815 % Kappa statistic 0.9699 Mean absolute error 0.0183 Root mean squared error 0.0956 Relative absolute error 3.6997 % Root relative squared error 19.2354 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 52.2222 % Total Number of Instances 270 8.3 J48 Classification Result for 14 Attributes: Correctly Classified Instances 239 88.5185 % Incorrectly Classified Instances 31 11.4815 % Kappa statistic 0.7653 Mean absolute error 0.1908 Root mean squared error 0.3088 Relative absolute error 38.6242 % Root relative squared error 62.1512 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 92.2222 % Total Number of Instances 270 8.4 ID3 Classification Result for selected Attributes using Fuzzy Rough Subset Evaluation: Correctly Classified Instances 270 100 % Incorrectly Classified Instances 0 0 % Kappa statistic 1 Mean absolute error 0 Root mean squared error 0 Relative absolute error 0 % Root relative squared error 0 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 25 % Total Number of Instances 270 8.5 J48 Classification Result for selected Attributes using Fuzzy Rough Subset Evaluation: Correctly Classified Instances 160 59.2593 % Incorrectly Classified Instances 110 40.7407 % Kappa statistic 0 Mean absolute error 0.2914 Root mean squared error 0.3817 Relative absolute error 99.5829 % Root relative squared error 99.9969 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 100 % Total Number of Instances 270 8.6 ID3 Classification Result for Information Gain Subset Evaluation Using Ranker Method: Correctly Classified Instances 270 100 % Incorrectly Classified Instances 0 0 % Kappa statistic 1 Mean absolute error 0 Root mean squared error 0 Relative absolute error 0 % Root relative squared error 0 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 33.3333 % Total Number of Instances 270 8.7 J48 Classification Result for Information Gain Subset Evaluation Using Ranker Method: Correctly Classified Instances 165 61.1111 % Incorrectly Classified Instances 105 38.8889 % Kappa statistic 0.3025 Mean absolute error 0.31 Root mean squared error 0.3937 Relative absolute error 87.1586 % Root relative squared error 93.4871 % Coverage of cases (0.95 level) 100 % Mean rel. region size (0.95 level) 89.2593 % Total Number of Instances 270 CONCLUSION: In this paper, from the above implementation result the Fuzzy Rough Subsets Evaluation is gives the selected attributes in less amount than the Info Gain Subset Evaluation and J48 decision tree classification techniques gives the approximate error rate using Fuzzy Rough Subsets Evaluation for the given data set than the ID3 decision tree techniques for both evaluation techniques. So finally for selecting the attributes fuzzy techniques gives the better result using Best First Search method and J48 classification method.

The Secret of Ella and Micha Chapter 3

Ella â€Å"I take it that's Micha?† Lila wanders around my kitchen as she tightens a loose ribbon on the waist of her floral dress. â€Å"He's even cuter than in the picture.† â€Å"Yep, that would be Micha.† I kick a box across the stained linoleum floor and flip the light on. It looks the same; seventies themed colors, wicker chairs around the glass table, and yellow and brown countertops. â€Å"So just your dad lives here?† Lila circles the small kitchen and her gaze lingers on the countertop next to the kitchen sink where empty bottles are lining the wall. â€Å"Yeah. My older brother moved out as soon as he graduated.† I adjust the handle of my bag and head for the stairway. The house smells like rotten food and smoke. In the living room, the aged plaid sofa is vacant, and the ash tray on the coffee table is spilling over with cigarette butts. The television is on so I shut it off. â€Å"So where's your dad?† Lila wonders as we climb up the stairs. â€Å"I'm not sure,† I avoid the truth, because he's probably at the bar. â€Å"Okay, where's your mom?† she probes. â€Å"You never told me where she lives.† Lila doesn't know much about me and it's how I want it. Leaving her in the dark, about my mom, my brother – everyone in this aspect of my life – has allowed me to transform into someone who doesn't have to deal with my problems. â€Å"My dad works nights,† I make up a story. â€Å"And my mom moved out quite a while ago. She lives up on Cherry hill.† She leans forward to study a portrait of my mother displayed on the wall; the same auburn hair, pale skin, and green eyes as me. Her smile was just as fake as mine, too. â€Å"Is this your mom?† She asks and I nod. â€Å"She looks just like you.† My chest tightens and I quickly trot to the top of the stairway. At the end of the hall, the bathroom door is wide open. The corner of the porcelain tub and the stain on the tile floor is in my line of vision. My heart constricts tighter as the memories flood me. I'm suffocating with panic. â€Å"Baby girl,† she said. â€Å"I'm going to go take a nap, just for a little while. I'll be back in just a bit.† My knees tremble as I shut the door. My chest opens up and oxygen flows through my lungs again. â€Å"So where does your brother live?† Lila peers inside my brother's room full of drums, guitar picks, CDs, and records. There's a bunch of band posters taped to the wall and a guitar up on a mount. â€Å"I think in Chicago.† â€Å"You think?† I shrug. â€Å"We don't have the best relationship. She nods, like she understands. â€Å"So is he in a band?† â€Å"I'm not sure if he's still in one now. I'm guessing since his stuff's here, probably not,† I say. â€Å"He only played because he was friends with Micha and he's in a band. Or was. I have no idea what he does anymore.† â€Å"Ella, did you lose touch with everyone in your life?† Lila accuses, tucking the pillow under her arm. Her scrutiny makes me uncomfortable. Avoiding confrontation, I turn on my bedroom light and shudder at the sight. It's like a museum of my past. Sheets of my artwork are tacked to the walls, trimmed with a black skeleton border Micha put up when we were twelve to make my room more â€Å"manly.† A collection of guitar picks line the far dresser and there is a pile of my boots in the corner. My bed is made with the same purple comforter and there's a plate with a half-eaten cookie on it, which is growing mold. I toss the cookie into the trash. Hasn't my dad been in here since I left? Lila picks up a guitar and plops down on the bed. â€Å"I didn't know you played.† She positions the guitar on her lap and strums the strings. â€Å"I always wanted to learn how to play, but my mom would never let me take lessons. You should teach me.† â€Å"I don't play.† I drop my bag on the floor. â€Å"That's Micha's guitar. His initials are on the back.† She turns it over and looks at the initials. â€Å"So the hot guy from next door is also a musician. God, I'm about to swoon.† â€Å"No swooning over anyone in this neighborhood,† I advise. â€Å"And since when are you into musicians? I have never, until today, heard you say anything about liking guys who can play the guitar.† â€Å"Since they look like him.† She points over her shoulder toward Micha's house, which is visible through the window of my room. â€Å"That boy is dripping with sexiness.† Jealousy growls in my chest and I mentally whisper for it to shut up. I pick up a photo of my mom and me at the zoo when I was six. We're happy, smiling, and the sun is bright against our squinting eyes. It rips at my heart and I let the photo fall back onto the desk. â€Å"There's a trundle under the bed that you can sleep on if you want.† â€Å"Sounds good.† She slides the guitar off her lap and goes over to the window, drawing the curtain back. â€Å"Maybe we should go to the party. It looks kind of fun.† I gather my hair away from my eyes before dragging the trundle out from under the bed. â€Å"No offense, Lila, but I don't think you can handle one of Micha's parties. Things can get a little bit crazy.† She narrows her eyes at me, insulted. â€Å"I can handle parties. It's you that never wanted to go to any of them. And the one's that I did talk you into going to, you just stood in the corner, drinking water and sulking.† I flop down on the bed with my arms and legs slack over the edges. â€Å"That party is nothing like a college frat party. They're the kind of parties you wake up from the next day on a park bench with no shoes on and a tattoo on your back, with no recollection of what happened the night before.† â€Å"Oh my God, is that how you got that tattoo on your back – the one you refuse to tell me what it means.† She lies on the bed next to me and we stare at the Chevelle poster on my ceiling. â€Å"It means infinite.† I tug the hem of my tank top down, hiding the tattoo on my lower back, and drape my arm over my forehead. â€Å"And I don't refuse to talk about it. I just can't remember how I got it.† She gives me a sad, puppy dog face and bats her eyelashes. â€Å"Pretty please, with a cherry on top. This might be my only chance to go to a party like this. The ones at my old neighborhood consist of limos, fancy dresses and tuxes, and a lot of champagne.† When I don't respond, she adds, â€Å"You owe me.† â€Å"How do you figure?† â€Å"For giving you a ride here.† â€Å"Please don't make me go down there,† I plead, clasping my hands together. â€Å"Please.† She rolls onto her stomach and props up on her elbows. â€Å"He's an old boyfriend, isn't he? You were lying. I knew it. No one can draw a picture like that of someone they've never loved.† â€Å"Micha and I have never dated.† I insist with a heavy sigh. â€Å"If you really want to go see what these parties are all about, I'll take you down there, but I'm not hanging around for more than five minutes.† I give in because deep down I'm curious to check up on the world I left behind. She claps her hands animatedly and squeals, looking out the window one last time. â€Å"Holy crap. Someone's standing on the roof.† They say curiosity killed the cat. â€Å"Come on, party girl. Let's get this over with.† *** About fifteen years ago, this town used to be a decent place to live. Then the factory that supplied jobs to almost the entire town shut down. People were laid off and slowly it began to dwindle into the bottomless pit that it is now. The houses across the street are painted in graffiti and I'm pretty sure my next door neighbor makes moonshine in his garage, or at least he did before I left. Inside Micha's house, there are people loitering in the entryway. I push my way through them and into the kitchen, which is crammed with even more people. On the table is a kegger and enough bottles of alcohol to open a liquor store. The atmosphere is overflowing with the scent of sweat and there are a few girls dancing on the kitchen counters. People are making out in the corners of the living room where the sofas are shoved to the side, so the band can flare on their instruments, screaming lyrics of pain and misunderstanding at the top of their lungs. I'm surprised Micha isn't up there playing. â€Å"Holy crap. This is†¦Ã¢â‚¬  Lila's blue eyes are round as she gawks at the people jumping up and down in the living room, shaking their bodies and thrashing their heads. â€Å"Like a mosh pit,† I finish for her, shoving a short girl with bleached hair out of my way. â€Å"Hey,† the girl whines as her drink spills down the front of her leather dress. â€Å"You did that on purpose.† For a split second, I forget who I am and turn around to blast her with a death glare. But then I remember that I'm the calm and rational Ella; one that doesn't get into fights and beat other girls up. â€Å"What, preppy girl?† She pats her chest, ready to throw down. â€Å"You think you scare me.† Lila bites her thumbnail. â€Å"We're sorry. She didn't mean to.† Chants fill the living room and the chaos is giving me a headache. â€Å"Sorry,† I strain an apology and squeeze between her and the wall. She snickers at me and her friends join in with her laughter as they sashay to the back door. It takes everything I have not to turn around and tackle her to the floor. Lila makes a beeline for the bar set up on the counter, dumps a drop of vodka into a cup, and mixes it with a splash of orange juice. â€Å"Okay, that was intense. I thought she was going to kick your ass.† â€Å"Welcome to Star Grove.† I shout over the music. â€Å"The Land of the Intense and Poverty-stricken, where the adolescents roam free without sober parental supervision and try to start fights wherever they can.† She laughs, takes a gulp of her drink, and her face pinches at the bitterness. â€Å"Try – † She starts, then coughs. She pounds her hand against her chest. â€Å"Are you going to make it?† I ask. Lila has never been a big drinker. She nods and clears her throat. â€Å"I was going to say try growing up where you have to get permission to wear a certain style of shoes.† I give her a mystified look and she adds, â€Å"If it wasn't up to my mother's stylish fashion standards I wasn't allowed to wear it.† I edge out of the way of a guy with blotchy skin and a beanie covering his head, who doesn't seem to mind that he knocks his shoulder into mine. â€Å"I'm sure it wasn't that bad growing up where you did. I mean, at least there was some control.† â€Å"Yeah, there was,† she says uneasily and her eyes quickly scan the room. â€Å"I can't believe there's a live band. It's like being at an outside concert.† â€Å"What? They don't have live bands in California?† I joke with a small smile as I pour myself a cup of water. â€Å"One's that take place outside?† She stirs her drink with a straw. â€Å"Not these kinds of bands. Think much more mellow, with a stage and seats to watch.† â€Å"Sounds like fun to me.† I oblige a smile and glance at my watch. â€Å"Are you about ready to go?† â€Å"Are you joking?† Sucking the drink out of the straw, she hops on the counter and crosses her legs. â€Å"We just got here. Why would we want to go? In fact, we should go dance.† My eyes find the living room, where a guy with dreads smashes his head against the glass plate of a cabinet in the corner and everyone cheers. â€Å"You can if you want, but I'm good.† I gulp my water. â€Å"I like all my bones intact.† Leaning against the counter, I scan through the crowd, curious to see where Micha is. I don't know why I'm so curious, but I am. Occasionally he would bail on his own parties, either to hook up or just get some quiet. I found him a couple of times hiding out on a lawn chair. Each time, he would pull me onto his lap and we would stare up at the night sky, talking about an unreachable future. I spot him in the corner, sitting on the couch with his arm draped around some blonde girl with boobs popping out of her dress. His hair hangs in his eyes and he's nibbling at his lip ring, driving the girl crazy I'm sure. They're just talking, but the girl keeps flipping her hair off her shoulder and her hand is on his chest. It's hard to tell if Micha's enjoying her company or not. He was always difficult to read when it came to girls because he never really looked interested in any of them, but sometimes he would end up with them for the night. I asked him about it once and he said it was all fun, but that he was just killing time until I gave into my inner desire to be with him. I tackled him to the ground for it and it made him laugh. â€Å"Why do you have that look on your face? Like you're undressing someone with your eyes?† Lila asks, following my gaze. â€Å"Oh, is that – â€Å" My eyes dart from Micha. â€Å"I wasn't looking at anyone, just the madness in the living room.† â€Å"Yeah, right,† she says, elevating her eyebrows. â€Å"You totally want him. I can see it on your face.† â€Å"Well, I'll be damned if it isn't the infamous Ella May!† Ethan Gregory grins from the other side of the counter, just behind Lila. He stumbles around the corner, nearly clipping his head on the low ceiling. Before I can respond, he has me trapped in an awkward hug with his long arms that are tracked with tattoos. His grey shirt smells like an ash tray and his breath like beer. He pulls back, ruffling his black hair with his fingers. â€Å"Does Micha know you're here in his house?† I lie breezily, very aware of where Micha is and what he's doing. â€Å"I'm pretty sure he saw me walk in.† â€Å"I doubt that. He's been looking for you for the past eight months.† He glances over his shoulder and nods at Lila, then tips in toward me. â€Å"You know he's been a wreck ever since you took off. You really fucked up his head, Ella.† â€Å"That's such a lie,† I tell him. Ethan and I have never really gotten along very well, which is why the hug confused me so much. We both had the same blunt attitude and butted heads a lot. The only reason we were friends at all was because of Micha. Although, there was one time we did bond for a split second, but we never talk about it. â€Å"Micha doesn't fall apart over anyone. I know him better than that.† His face is flushed and his brown eyes are bloodshot. â€Å"I guess you don't know him as well as you think then, because he's been a wreck. In fact, all he's done for the last few months is search for you.† â€Å"Which explains the party,† I retort. â€Å"I'm guessing that classifies as that.† â€Å"First one in five months,† he says. â€Å"And I think he only did it because he found out where you were and needed a distraction.† â€Å"I know him better than you do, Ethan, and he doesn't fall apart over girls,† I say, but cringe at the fact that I might not know him anymore. A lot can happen in eight months. â€Å"Hey, Lila, we should go. It's getting late.† She glances at her diamond encrusted Rolex and rolls her eyes. â€Å"It's like nine thirty.† â€Å"You're leaving already?† He waves his hand in the air. â€Å"That's nonsense talk right there. You haven't even seen Micha yet and he's gonna be super pissed if he misses seeing you, especially since you ran away from him in the driveway.† â€Å"Actually, I think we're going to hang out for a little while longer,† Lila presses with unrelenting eyes. She mouths, he's hot. Then she fastens her hands together. Please, Ella. Pretty please. Ethan isn't Lila's type. He's got baggage almost as heavy as mine. I start to protest when Micha's deep voice floats over my shoulder and tickles my skin like feathers. Without being able to help it, I let out a soft moan. â€Å"Yeah, pretty girl, stay a little longer.† He's so close that the heat of his body kisses my skin and my insides tremor. His fingers comb through my hair as he whispers, â€Å"You smell so good. God I've missed your smell.† â€Å"I have to get up really early in the morning.† I clear my throat and Lila's eyebrows furrow. â€Å"I need to go home and get some sleep.† He places his hand on the counter, so the crook of his arm is touching my hip. â€Å"You can keep trying to avoid me,† he breathes in my ear, taking a nip at my earlobe. â€Å"But sooner or later you're going to have to talk to me.† His breath reeks of beer and his clothes of smoke. Refusing to crack at the sound of his sexy voice, I turn and face him. â€Å"I don't have time to get drunk and act like a moron.† He's even more gorgeous under the light and more irresistible, even though his eyes are glossed over. â€Å"It's your fault I'm drunk – you drive me crazy.† He descends his voice to a soft purr, the same voice he's used on me many times to get what he wants – the voice that makes me feel alive inside. â€Å"Baby, come on. Please. We need to talk.† He leans in to kiss me. The suddenness throws me off balance and I trip over my own feet. â€Å"Micha, stop it.† I gently push him back and he staggers into the edge of the counter. â€Å"You're drunk. And I'm going home.† â€Å"She's acting weird†¦ like she's way too calm,† Ethan remarks, with a wave of his finger. â€Å"And she's dressed funny, like that girl we use to go to school with. What's her name?† He snaps his fingers. â€Å"Stacy†¦. Stacy†¦Ã¢â‚¬  â€Å"Harris,† I say exhausted. â€Å"And I look like a girl that went away to college and grew up.† Lila slants forward. â€Å"Ella's been this way since I've known her, but I'm really curious what she used to look like with the way everyone keeps talking about her because I can't picture her any other way besides this.† Micha and Ethan trade drunken looks and then howl with laughter. The room quiets down a little as people glance in our direction. â€Å"What's so funny?† Lila frowns and looks to me for help. â€Å"I'm so lost.† â€Å"Nothing. They just think they're funny.† I dodge around Micha, but he seizes my elbow and hauls me back against his chest. â€Å"Hey relax, baby.† He kisses my forehead and gives me his innocent face. â€Å"Please don't go. I just got you back.† Before I took off, the boundaries of our friendship were starting to blur. I thought time would fix this, but it seems like we're back to where we started. As much as I would love to melt into him, it just can't happen. I can't open up like that and lose control. I need control. â€Å"No one's got me back. I'm just here for summer break and only because I didn't have money to rent an apartment,† I say and his expression falls. â€Å"The Ella you knew is gone. She died on that bridge eight months ago.† He blinks, as shocked as I am. His lips part then he clasps them shut, struck speechless. â€Å"I didn't mean that,† I say quickly. â€Å"I'm sorry, Micha. I just can't deal with this.† â€Å"Don't be sorry for being real,† he says, rubbing his forehead with the back of his hand. I force the lump in my throat down. â€Å"I'm sorry,† I say again, and then weave through the crowd and out the back door, inhaling the fresh air. â€Å"What's your problem?† Lila asks as she catches up with me at the edge of my driveway. She squashes her plastic cup and tosses it into the trash can on the back porch. â€Å"I'm so confused. What just happened?† â€Å"I needed to get out of there before I lost it.† I don't slow down until I'm in my room where I close the door and shut the window, locking away the world. I sigh back against the wall, breathing in the quiet. Lila watches me with inquisitiveness as she pulls her hair back into a bun and puts some lip gloss on. â€Å"Ethan and Micha act like you used to be someone else. Like this isn't the real you. Want to explain?† â€Å"Not really.† I push away from the door and collect some pajamas from the duffel bag. â€Å"I'm going to go take a shower. Do you need anything from downstairs?† â€Å"Yeah, for you to tell me why those guys have you so frazzled.† She unclips her watch and tosses it into her purse that's on the bed. â€Å"I've never seen you so worked up like that. You basically had an orgasm when you first saw him.† â€Å"I did not,† I say, embarrassed and annoyed. â€Å"And you haven't seen me that worked up because I'm not that person anymore.† â€Å"Except for when you're around him,† she insinuates. â€Å"When you were talking to him, there was something in your eyes I've never seen before. You were always so closed off to all the guys at parties and in school. Honestly, I thought you were a virgin. But the way you and Micha were looking at each other – you've had sex with him, right?† Pressing my lips together, I tuck my pajamas under my arm, and shake my head. â€Å"No, Micha and I've never slept together, just like we've never dated. But we've been friends since we were kids.† She sits down on the bed and unhooks her sandal. â€Å"But you've had sex before?† I squirm in my skin. â€Å"I'm going to go get ready for bed.† â€Å"Whoa, wait a second.† She leaps off the bed wearing one shoe and jumps in front of the door with her hands spread out to the side. â€Å"Are you saying that you've never had sex? Ever.† I struggle for words she'll understand. â€Å"It's not like I haven't because I don't believe in premarital sex or anything. I just†¦ Look there's a lot you don't know about me and sometimes I have a hard time getting close to people.† She's not surprised. â€Å"Well, obviously. That's totally been a given from day one.† â€Å"What do you mean?† I question. â€Å"I've never told anyone that before.† Not even Micha. â€Å"It means sometimes I can see right through you.† She sighs and counts down on her fingers. â€Å"I've been your roommate for eight months and all I know about you is you're focused on school, you hate to drink, hate being around large crowds, and have never went on a date. I barely know you and being here, I'm starting to wonder if I know you at all.† She knows the Ella I want her to know. â€Å"Can you let me by? I'm really tired.† She gives me a disbelieving look, but doesn't press. She steps aside and lets me by. Relief washes over me because I don't want to get into it with her. Not tonight. Not ever. I never want to get into the night that changed my life. I buried my reckless identity, and I won't dig it up again.

Universal Soldier

As a college student in the early 1960s Buffy Sainte-Marie became known as a writer of protest songs and love songs. But unknown to most of the mainstream public, she was even then spending as much time around the drum at a small Indian reserve in Canada as she was in front of a microphone on the concert stages of the world. Having written â€Å"Universal Soldier†, one of the anthems of the 60s peace movement, she was nonetheless absent from the big mass protest marches, in favor of shedding her light on Indian rights and environmental issues, which she still does today. Analysis of the song Universal Soldier Five foot two and six foot four were the height parameters for soldiers in 1961. Fighting with missiles and with spears symbolize the future and the past, soldiers are soldiers: only the equipment is different. The ages 17 to 31 were age parameters to be a soldier during the 1960s and soldiers have been around for centuries. Soldiers are also religious people and not confined to just one religion and though religion forbids it, he chooses to be a killer. No matter what side he’s on, it’s still absurd. Soldiers are not just from some far away enemy country but from â€Å"our† country too thinking that fighting will end all fighting. Soldiers are on all sides using violence as an act of peace having a responsibility overlooked for humanity. Soldiers learn nothing from history so do not see obvious outcomes of repeating it. We can't blame just the leaders and each individual has a choice. We are all responsible – civilians, voters and soldiers.

Analysis Of The Book Steve Jobs By Walter Isaacson

A book review of â€Å"Steve Jobs† by Walter Isaacson Walter Isaascon’s account of Steve Jobs in some way is a product developed from the mind of its subject. Even though Steve Jobs was categorical that he would not interfere with the creation of book, he handpicked Walter Isaacson to pen his legacy for all. The fact that he settled for Isaacson does not come as a surprise. While it may be agreed that great men are always not nice men, it can be excused if they are geniuses. Isaacson started his examination of rare intellects in 1986 when he co-authored â€Å"The Wise Men’, a group of people who rebuilt the world after World War 2. Additionally, his biographies of Alber Einstein and Franklin are engrossing, readable and epic studies of men who changed the world. One wonders whether all these efforts served as warm-up exercises for Walter Isaacson prior to tackling not just the unsavory life of Steve Jobs but also his place in the economic and industrial phenomen on of the era of computer technology and its many applications. The fact that Jobs perceived himself in this light is neither unjustified nor shocking. Whereas Isaacson does not shy away from Jobs’ vitriolic temper, it is evident that in some respects,â€Å" Steve Jobs†is a biography told through the discussed â€Å"reality distortion† view of Steve Jobs himself(Leith). Even though other views are presented, Steve Jobs always has the last, candid word. It appears that Steve Jobs was not a lively man. Abandoned by his mother, he would,Show MoreRelatedEffective Leadership Skills Showed By Steve Jobs1293 Words   |  6 Pagesfocused on the effective leadership skills showed by Steve Jobs over a supported timeframe and the turnaround that Apple had experienced under his vision and orders. Steve Jobs played a very important role in leadership that led Apple from a company, which was founded in a car-garage to a great Macintosh Pcs, iPod, iPhone and iPad. The report will additionally break down and look at the key administrative and initiative abilities that Steve Jobs aced that prompted to the grand development of AppleRead MoreBarnes and Noble: Business Information System5010 Words   |  21 PagesAcademic Registrar use only | Table of Content Executive Summary 3 Company Description 5 Business Perspective Evaluation 6 Establishment and Organization of E-commerce 6 E-commerce and Revenue models 8 Analysis of Industry, Market and Competitors 9 SWOT analysis 10 Competitors 11 Promotion and Customer Relations 12 Market Target 12 Advertising Methods 12 Customer Relationship Management 13 Technological Perspective 13 E-commerce framework 13 People 13 Public policyRead MoreInnovators Dna84615 Words   |  339 Pagesdisruptive innovation. â€Å"Businesses worldwide have been guided and in uenced by e Innovator’s Dilemma and e Innovator’s Solution. Now e Innovator’s DNA shows where it all starts. is book gives you the fundamental building blocks for becoming more innovative and changing the world. One of the most important books to come out this year, and one that will remain pivotal reading for years to come.† Chairman and CEO, salesforce.com; author, Behind the Cloud â€Å" e Innovator’s DNA is the ‘how to’