Data Mining or Knowledge Discovery

Data Mining or Knowledge Discovery SYNOPSIS INTRODUCTION Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining or knowledge discovery, is the computed assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of data. Data sets of very high dimensionality, such as microarray data, pose great challenges on efficient processing to most existing data mining algorithms. Data management in high dimensional spaces presents complications, such as the degradation of query processing performance, a phenomenon also known as the curse of dimensionality. Dimension Reduction (DR) tackles this problem, by conveniently embedding data from high dimensional to lower dimensional spaces. The dimensional reduction approach gives an optimal solution for the analysis of these high dimensional data. The reduction process is the action of diminishing the variable count to few categories. The reduced variables are new defined variables which are the combinations of either linear or non-linear combinations of variables. The reduction of variables to a clear dimension or categorization is extracted from the unusual dimensions, spaces, classes and variables. Dimensionality reduction is considered as a powerful approach for thinning the high dimensional data. Traditional statistical approaches partly calls off due to the increase in the number of observations mainly due to the increase in the number of variables correlated with each observation. Dimensionality reduction is the transformation of High Dimensional Data (HDD) into a meaningful representation of reduced dimensionality. Principal Pattern Analysis (PPA) is developed which encapsulates feature extraction and feature categorization. Multi-level Mahalanobis-based Dimensionality Reduction (MMDR), which is able to reduce the number of dimensions while keeping the precision high and able to effectively handle large datasets. The goal of this research is to discover the protein fold by considering both the sequential information and the 3D folding of the structural information. In addition, the proposed approach diminishes the error rate, significant rise in the throughput, reduction in missing of items and finally the patterns are classified. THESIS CONTRIBUTIONS AND ORGANIZATION One aspect of the dimensionality reduction requires more studies to find out how the evaluations are performed. Researchers find to finish the evaluation with a sufficient understanding of the reduction techniques so that they can make a decision to use its suitability of the context. The main contribution of the work presented in this research is to diminish the high dimensional data into the optimized category variables also called reduced variables. Some optimization algorithms have been used with the dimensionality reduction technique in order to get the optimized result in the mining process. The optimization algorithm diminishes the noise (any data that has been received, stored or changed in such a manner that it cannot be read or used by the program) in the datasets and the dimensionality reduction diminishes the large data sets to the definable data and after that if the clustering process is applied, the clustering or any mining results will yield the efficient results. The organization of the thesis is as follows: Chapter 2 presents literature review on the dimensionality reduction and protein folding as application of the research. At the end all the reduction technology has been analyzed and discussed. Chapter 3 presents the dimensionality reduction with PCA. In this chapter some hypothesis has been proved and the experimental results has been given for the different dataset and compared with the existing approach. Chapter 4 presents the study of the Principal Pattern Analysis (PPA). It presents the investigation of the PPA with other dimensionality reduction phase. So by the experimental result the obtained PPA shows better performance with other optimization algorithms. Chapter 5 presents the study of PPA with Genetic Algorithm (GA). In this chapter, the procedure for protein folding in GA optimization has been given and the experimental result shows the accuracy and error rate with the datasets. Chapter 6 presents the results and discussion of the proposed methodology. The Experimental results shows that PPA-GA gives better performance compared than the existing approaches. Chapter 7 concludes our research work with the limitation which the analysis has been made from our research and explained about the extension of our research so that how it could be taken to the next level of research. RELATED WORKS (Jiang, et al. 2003) proposed a novel hybrid algorithm combining Genetic Algorithm (GA). It is crucial to know the molecular basis of life for advances in biomedical and agricultural research. Proteins are a diverse class of biomolecules consisting of chains of amino acids by peptide bonds that perform vital functions in all living things. (Zhang, et al. 2007) published a paper about semi supervised dimensionality reduction. Dimensionality reduction is among the keys in mining high dimensional data. In this work, a simple but efficient algorithm called SSDR (Semi Supervised Dimensionality Reduction) was proposed, which can simultaneously preserve the structure of original high dimensional data. (Geng, et al. 2005) proposed a supervised nonlinear dimensionality reduction for visualization and classification. Dimensionality reduction can be performed by keeping only the most important dimensions, i.e. the ones that hold the most useful information for the task at hand, or by projecting the original data into a lower dimensional space that is most expressive for the task. (Verleysen and Franà §ois 2005) recommended a paper about the curse of dimensionality in data mining and time series prediction. The difficulty in analyzing high dimensional data results from the conjunction of two effects. Working with high dimensional data means working with data that are embedded in high dimensional spaces. Principal Component Analysis (PCA) is the most traditional tool used for dimension reduction. PCA projects data on a lower dimensional space, choosing axes keeping the maximum of the data initial variance. (Abdi and Williams 2010) proposed a paper about Principal Component Analysis (PCA). PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. The goal of PCA are to, Extract the most important information from the data table. Compress the size of the data set by keeping only this important information. Simplify the description of the data set. Analyze the structure of the observations and the variables. In order to achieve these goals, PCA computes new variables called PCA which are obtained as linear combinations of the original variables. (Zou, et al. 2006) proposed a paper about the sparse Principal Component Analysis (PCA). PCA is widely used in data processing and dimensionality reduction. High dimensional spaces show surprising, counter intuitive geometrical properties that have a large influence on the performances of data analysis tools. (Freitas 2003) proposed a survey of evolutionary algorithms of data mining and knowledge discovery. The use of GAs for attribute selection seems natural. The main reason is that the major source of difficulty in attribute selection is attribute interaction. Then, a simple GA, using conventional crossover and mutation operators, can be used to evolve the population of candidate solutions towards a good attribute subset. Dimension reduction, as the name suggests, is an algorithmic technique for reducing the dimensionality of data. The common approaches to dimensionality reduction fall into two main classes. (Chatpatanasiri and Kijsirikul 2010) proposed a unified semi supervised dimensionality reduction framework for manifold learning. The goal of dimensionality reduction is to diminish complexity of input data while some desired intrinsic information of the data is preserved. (Liu, et al. 2009) proposed a paper about feature selection with dynamic mutual information. Feature selection plays an important role in data mining and pattern recognition, especially for large scale data. Since data mining is capable of identifying new, potential and useful information from datasets, it has been widely used in many areas, such as decision support, pattern recognition and financial forecasts. Feature selection is the process of choosing a subset of the original feature spaces according to discrimination capability to improve the quality of data. Feature reduction refers to the study of methods for reducing the number of dimensions describing data. Its general purpose is to employ fewer features to represent data and reduce computational cost, without deteriorating discriminative capability. (Upadhyay, et al. 2013) proposed a paper about the comparative analysis of various data stream procedures and various dimension reduction techniques. In this research, various data stream mining techniques and dimension reduction techniques have been evaluated on the basis of their usage, application parameters and working mechanism. (Shlens 2005) proposed a tutorial on Principal Component Analysis (PCA). PCA has been called one of the most valuable results from applied linear algebra. The goal of PCA is to compute the most meaningful basis to re-express a noisy data set. (Hoque, et al. 2009) proposed an extended HP model for protein structure prediction. This paper proposed a detailed investigation of a lattice-based HP (Hydrophobic – Hydrophilic) model for ab initio Protein Structure Prediction (PSP). (Borgwardt, et al. 2005) recommended a paper about protein function prediction via graph kernels. Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence. Simulating the molecular and atomic mechanisms that define the function of a protein is beyond the current knowledge of biochemistry and the capacity of available computational power. (Cutello, et al. 2007) suggested an immune algorithm for Protein Structure Prediction (PSP) on lattice models. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. (Yamada, et al. 2011) proposed a paper about computationally sufficient dimension reduction via squared-loss mutual information. The purpose of Sufficient Dimension Reduction (SDR) is to find a low dimensional expression of input features that is sufficient for predicting output values. (Yamada, et al. 2011) proposed a sufficient component analysis for SDR. In this research, they proposed a novel distribution free SDR method called Sufficient Component Analysis (SCA), which is computationally more efficient than existing methods. (Chen and Lin 2012) proposed a paper about feature aware Label Space Dimension Reduction (LSDR) for multi-label classification. LSDR is an efficient and effective paradigm for multi-label classification with many classes. (Brahma 2012) suggested a study of algorithms for dimensionality reduction. Dimensionality reduction refers to the problems associated with multivariate data analysis as the dimensionality increases. There are huge mathematical challenges has to be encountered with high dimensional datasets. (Zhang, et al. 2013) proposed a framework to inject the information of strong views into weak ones. Many real applications involve more than one modal of data and abundant data with multiple views are at hand. Traditional dimensionality reduction methods can be classified into supervised or unsupervised, depending on whether the label information is used or not. (Danubianu and Pentiuc 2013) proposed a paper about data dimensionality reduction framework for data mining. The high dimensionality of data can cause also data overload, and make some data mining algorithms non applicable. Data mining involves the application of algorithms able to detect patterns or rules with a specific means from large amounts of data, and represents one step in knowledge discovery in database process. OBJECTIVES AND SCOPE OBJECTIVES Generallydimension reduction is the process of reduction of concentrated random variable where it can be divided into feature selection and feature extraction. The dimension of the data depends on the number of variables that are measured on each investigation. While scrutinizing the statistical records data accumulated in an exceptional speed, so dimensionality reduction is an adequate approach for diluting the data. While working with this reduced representation, tasks such as clustering or classification can often yield more accurate and readily illustratable results, further the computational costs may also be greatly diminished. A different algorithm called Principal Pattern Analysis (PPA) is presented in this research. Hereby the desire of dimension reduction is enclosed. The description of a diminished set of features. For a count of learning algorithms, the training and classification times increase precisely with the number of features. Noisy or inappropriate features can have the same influence on the classification as predictive features, so they will impact negatively on accuracy. SCOPE The scope of this research is to present an ensemble approach for dimensionality reduction along with pattern classification. Dimensionality reduction is the process of reduction the high dimensional data i.e., having the large features in the datasets which contain the complicated data. The usage of this dimensionality reduction process yields many useful and effective results over the process in mining. The former used many techniques to overcome this dimensionality reduction problem but they are having certain drawbacks to it. The dimensional reduction technique enriches the execution time and yields the optimized result for the high dimensional data. So, the analysis states that before going for any clustering process, it is suggested for a dimensional reduction process of the high dimensional datasets. As in the case of dimensionality reduction, there are chances of missing the instruction. So the approach which is used to diminish the dimensions should be more corresponding to the whole datasets. RESEARCH METHODOLOGY The scope of this research is to present an ensemble approach for dimensionality reduction along with the pattern classification. Problems on analyzing High Dimensional Data are, Curse of dimensionality Some important factors are missed Result is not accurate Result is having noise. In order to mine the surplus data besides estimating gold nugget (decisions) from data involves several data mining techniques. Generally the dimension reduction is the process of reduction of concentrated random variables where it can be divided into feature selection and feature extraction. PRINCIPAL PATTERN ANALYSIS The Principal Component Analysis decides the weightage of the respective dimension of a database. It is required to reduce the dimension of the data (having less features) in order to improve the efficiency and accuracy of data analysis. Traditional statistical methods partly calls off due to the increase in the number of observations, but mainly because of the increase in number of variables associated with each observation. As a consequence an ideal technique called Principal Pattern Analysis (PPA) is developed which encapsulates feature extraction and feature categorization. Initially it applies Principal Component Analysis (PCA) to extract Eigen vectors similarly to prove pattern categorization theorem the corresponding patterns are segregated. The major difference between the PCA and PPA is the construction of the covariance matric. PPA algorithm for the dimensionality reduction along with the pattern classification has been introduced. The step by step procedure has been given as follows: Compute the column vectors such that each column is with M rows. Locate the column vectors into single matrix X of which each column has M x N dimensions. The empirical mean EX is computed for M x N dimensional matrix. Subsequently the correlation matric Cx is computed for M x N matrix. Consequently the Eigen values and Eigen vectors are calculated for X. By interrupting the estimated results, the PPA algorithm persists by proving the Pattern Analysis theorem. FEATURE EXTRACTION Feature extraction is an exception form of dimensionality reduction. It is needed when the input data for an algorithm is too large to be processed and it is suspected to be notoriously redundant then the input data will be transformed into a reduced representation set of features. By the way of explanation transforming the input data into the set of features is called feature extraction. It is expected that the feature set will extract the relevant information from the input data in order to perform the desired task using the reduced information of the full size input. ESSENTIAL STATISTICS MEASURES CORRELATION MATRIX A correlation matrix is used for pointing the simple correlation r, among all possible pairs of variables included in the analysis; also it is a lower triangle matrix. The diagonal elements are usually omitted. BARTLETT’S TEST OF SPHERICIY Bartlett’s test of Sphericity is a test statistic used to examine the hypothesis that the variables are uncorrelated in the population. In other words, the population correlation matric is an identity matrix; each variable correlates perfectly with itself but has no correlation with the other variables. KAISER MEYER OLKIN (KMO) KMO is a measure of sampling adequacy, which is an index. It is applied with the aim of examining the appropriateness of factor/Principal Component Analysis (PCA). High values indicate that factor analysis benefits and their value below 0.5 imply that factor suitable may not be suitable. 4.3.4MULTI-LEVEL MAHALANOBIS-BASED DIMENSIONALITY REDUCTION (MMDR) Multi-level Mahalanobis-based Dimensionality Reduction (MMDR), which is able to reduce the number of dimensions while keeping the precision high and able to effectively handle large datasets. MERITS OF PPA The advantages of PPA over PCA are, Important features are not missed. Error approximation rate is also very less. It can be applied to high dimensional dataset. Moreover, features are extracted successfully which also gives a pattern categorization. CRITERION BASED TWO DIMENSIOANL PROTEIN FOLDING USING EXTENDED GA Extensively, protein folding is the method by which a protein structure deduces its functional conformation. Proteins are folded and held bonded by several forms of molecular interactions. Those interactions include the thermodynamic constancy of the complex structure, hydrophobic interactions and the disulphide binders that are formed in proteins. Folding of protein is an intricate and abstruse mechanism. While solving protein folding prediction, the proposed work incorporates Extended Genetic Algorithm with Concealed Markov Model (CMM). The proposed approach incorporates multiple techniques to achieve the goal of protein folding. The steps are, Modified Bayesian Classification Concealed Markov Model (CMM) Criterion based optimization Extended Genetic Algorithm (EGA). 4.4.1MODIFIED BAYESIAN CLASSIFICATION Modified Bayesian classification method is used grouping of protein sequence into its related domains such as Myoglobin, T4-Lysozyme and H-RAS etc. In Bayesian classification, data is defined by the probability distribution. Probability is calculated that the data element ‘A’ is a member of classes C, where C = {C1, C2 †¦ CN}. (1) Where, Pc(A) is given as the density of the class C evaluated at each data element.

Case Study Cicso Essay

How does building a brand in a business-to-business context different from doing so in the consumer market? When companies market (company A) their products to other business (Company B), they are looking to build a lasting business relationship. Company B is marketing their product and services because they know and understand what the company will need in order to operate more efficiently. When companies make purchases, it is a multi-step process that involves executive decisions and planning, company financial review (depending on the amount of the purchase), and possible sales meetings to offer demonstrations of new products (Business Marketing Association, n.d.). B2B marketing is to convert prospects into customers and build a lasting business relationship; they need to focus on relationship building and communication using marketing activities that generate leads that can be nurtured during the sales cycle (Murphy, 2007). Marketing to a company can be done through email, webcasting, newsletters, telemarketing, direct mail, and representative follow up services. Companies keep in constant contact with the business in an attempt to keep doing business with them and ensuring that any needs the company may have, they will attempt to meet or exceed. When companies decide to market to consumers, they use a different approach. The majority of the products on the market for consumers are not a necessity; companies have to use creative ways to ensure that consumers will purchase the product. The ultimate goal of B2C marketing is to convert shoppers into buyers as aggressively and consistently as possible (Murphy, 2007). Unlike how companies make decisions, consumers go off their emotions, product eye appeal, prices, discounts, and coupon usage. When consumers decided to make purchases, the buying process starts long before the actual purchase and has consequences long afterwards (Kotler & Keller, 2012). Since consumers make purchase for  different reasons than companies, consumers face a higher risk because of factors that may not be in their control. Technology has made marketing easier and even free for some companies. When companies target consumers, they use social media, blogs, electronic coupons, and customer survey completions that offer winnings. Consumers review the advertisements and see them as a good deal, even if it’s for a product they don’t need. To make the deal even better companies also offer loyalty rewards for frequent shoppers and buyers. Companies combine merchandise and education to consumers to keep the coming back (Murphy, 2007). This marketing technique lets the company know that the customer will return to make purchases and even purchase new products when they come on the market. Business Marketing Association, n.d. Key differences between B2B and consumer marketing. Retrieved from http://www.marketing.org/i4a/pages/index.cfm?pageID=3418 Kotler. P., Keller, K. L., 2012. Marketing Management (14th ed.). Upper Saddle, NJ: Prentice Hall Murphy, D. 2007, Marketing for B2B vs. B2C similar but different. Retrieved from http://masterful-marketing.com/marketing-b2b-v s-b2c/ Is Cisco’s plan to reach out to consumers a viable one? Why or why not? Cisco’s plan to reach consumers looks like a very viable plan. The company was able to launch a campaign that introduced them to consumers as being able to service not only large companies, but also to everyday consumers. Most consumers may have never thought about wireless capabilities being used within their homes; however Cisco was looking to change that by offering wireless network options to consumers. The â€Å"Human Network† campaign tried to humanize the technology giant and the initial results were positive (Kotler & Keller, 2012). Cisco Connected Sports allowed the company to showcase their product in a large venue. The fans who attended the games were able to see the new technology being used and how the stadium made the devices seem easy to use and navigate. Serious sports fans would be pleased, but the company still needs to think about consumers that do not attend games. The downside of Cisco entering the consumer market is that they had a lot of competition when attempting to market to consumers. While the company does have a viable plan to obtain the customers attention, the downside is that they have to compete with well-known electronic companies. For instance, vendors such as Samsung Electronics, which have long experience and established brands in that business, can fairly easily add networking to their products; its less likely that companies out of the network and into the living room Lawson, 2013). According to Kotler & Keller, (2012), Cisco’s revenues increased 41 percent from 2006 to 2008, led by sales increases in both home and business use; by the end of 2008, Cisco’s revenue topped 39.5 billion and Business Week ranked it the 18th biggest global brand (p. 57). Even with the increased revenue, Cisco sold its home networking business to Belkin International; the company plans to fold Linksys’ employee and products into its operations while keeping the Linksys brand alive (Lawson, 2013). Since the sale of Linksys to Belkin, Cisco has once again attempted to enter the consumer market by offering services through a service provider. The essence of Cisco’s business with services providers, where it makes both set-top boxes for homes and back-end infrastructure for content delivery (Lawson, 2013). Cisco could possibly make a comeback into the consumer market and if Cisco wants to be part of that, they will need to market and introduce products that consumers really need and want. Lawson, S., 2013. After selling Linksys, Cisco aims to reach consumers through carriers. Retrieved from http://www.computerworld.com/s/article/9236213/After_selling_Linksys_Cisco_aims_to_reach_consumers_through_carriers Kotler. P., Keller, K. L., 2012. Marketing Management (14th ed.). Upper Saddle, NJ: Prentice Hall

Essay on Raising a Child - 879 Words

Becoming a mother was one of the most stressful times in my life. I found out quick that the advice from the members of the medical profession did not always agree with advice that was passed down from the older generation of my family. For example, the advice on introducing cereal to an infant’s last bottle at night for a few extra hours of sleep, or being advised to wake an infant up every two hours in the beginning to feed them, or the most confusing to me the advice on punishing children in general. Becoming a parent (especially at a young age), I found it difficult to separate the real and useful advice from the useless or unimportant advice. I became worried that I maybe really had no idea what I was doing and it became easy for†¦show more content†¦I have to admit it worked. I was so happy that at my daughters next well check up I told the physician what I had been doing to only get a scolding. Turns out that she could have aspirated on this new formula concoct ion and it could stimulate her to over eat in other feedings during the day. He then said, I should stop and start it when she reached the 4 month mark at least. The day after I delivered my daughter I was sitting in my postpartum recovery room holding her while she was sleeping; just looking at her, when her nursery nurse came in and asked me all types of questions that ended with her asking when was the last time I fed her. She then proceeded to tell me that I needed to wake up my baby every 2 hours to ensure that I established my milk supply if I was going to breastfeed, ensure she was growing appropriately, and it would also help her learn a set schedule for eating. I took her advice and it made since but, then my mother-in law had to state her opinion when the nurse left the room. She said, â€Å"Never wake a sleeping baby.† It was natural for them to sleep, they are growing and they will do it on their own schedule. I could use a breast pump to encourage my milk to come in, if I thought I needed to. When my daughter was 2 my husband and I went through a period where we butted heads on how we were going to discipline her. Both of us growing up were spanked as a form of discipline and I felt the pressure to raise our child the same. I have to admit that IShow MoreRelatedRaising My Virtual Child : Raising A Child1822 Words   |  8 Pages Raising My Virtual Child – Smarika Amrit Raj Subedi PSYC 2314 North Lake College â€Æ' Raising My Virtual Child – Smarika Raising a Virtual Child had been a great experience for me. This assignment made me better understand why adolescents are the way they are. I had my own expectation before the onset of this project, how my virtual child was going to be at age of 18? 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Quotation Analysis Of Gilgamesh - 1270 Words

Part A: Quotation Analysis A. The similes used by the speaker help depict his lover’s image. He compares the movement of the separate strands of her hair like goats traveling down the side of a mountain. The speaker also compares when her teeth first appear as she smiles like a flock of sheep that arise after being washed. In his similes, the depiction of the flock of animals is repeated by the speaker in order to show that his lover is very fertile and the two should stick together. This section of the text is similar to the other parts of the poem since the two lovers are completely infatuated with one another and enjoy expressing their emotions. B. The narrator’s purpose for including this hyperbolic description is to describe the†¦show more content†¦D. Daedalus carefully explains to his son, Icarus, how to use the wings he has invented. Even though Icarus is very mischievous, Daedalus trusts that he will take his warnings into consideration; however, his warning foreshadows Icarus’ downfall. His instructions connect to Icarus’ death since Icarus both melts the wax from the feathers and becomes too heavy to escape the waters. Given Daedalus’ instructions, Icarus was determined to fail since he had to delicately fly in between the waves and the sun, no explicit boundaries are in place. It is ironic that Daedalus fails to predict his son’s behavior, yet he equips Icarus with a such an instrument that requires a high skill to operate; therefore, Daedalus indirectly kills his only son. Part B: Essay In The Epic of Gilgamesh, The Odyssey, and The Ramayana of Valmiki, the protagonists Gilgamesh, Odysseus, and Rama all share similar traits. Although the three characters each came from different backgrounds and accomplished different tasks, they all share the qualities of a hero. The three exhibit strength and wisdom, the ability to defeat god-like creatures, and are able to overcome the obstacles in their lives. In order to be labeled as a hero, characters are usually stronger than or go through enough hardship to become stronger than their obstacles. In the introduction to The Epic of Gilgamesh, Gilgamesh is portrayed as the mightiest man of the