INTRODUCTION TO DATA MINING VIPIN KUMAR PDF
Introduction to Data Mining Vipin Kumar, University of Minnesota Provides both theoretical and practical coverage of all data mining topics. Cluster Analysis: Basic Concepts and Algorithms [PPT] [PDF] (last updated: 14 Feb, ). Vipin Kumar Discuss whether or not each of the following activities is a data mining Suppose that you are employed as a data mining consultant for an In-. Download or Read Online eBook introduction to data mining tan pdf in PDF Format From The Vipin Kumar, Data mining course at University of Minnesota.
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A survey of clustering techniques in data mining, originally Overview Specifically, this book provides a comprehensive introduction to data. Kumar,. Vipin, author. Title: Introduction to Data Mining / Pang-Ning of Minnesota, Anuj Karpatne, University of Minnesota, Vipin Kumar, University of. Jiawei Han and Micheline Kamber, “Data Mining: Concepts and. Techniques” Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, "Introduction to Data.
Because of the exponential size of its search space, the goal of association analysis is to extract the most interesting patterns in an efficient manner.
Introduction to Data Mining (First Edition)
Useful applications of association analysis include finding groups of genes that have related functionality, identi- fying Web pages that are accessed together, or understanding the relationships between different elements of Earth's climate system. Example 1. The transactions shown in Ta- ble 1. Association analysis can be applied to find items that are fre- quently bought together by customers.
This type of rule can be used to identify potential cross-selling opportunities among related items. Cluster a nalysis seeks to find groups of closely related observations so that observations that belong to the same cluster are more similar to each other 10 Chapter 1 Introduction Table 1. Market basket data. Clustering has been used to group sets of related customers, find areas of the ocean that have a significant impact on the Earth's climate, and compress data.
The collection of news articles shown in Table 1.
Each article is represented as a set of word-frequency pairs w, c , where w is a word and c is the number of times the word appears in the article. There are two natural clusters in the data set. The first cluster consists of the first four ar- ticles, which correspond to news about t he economy, while the second cluster contains the last four articles, which correspond to news about health care. A good clustering algorithm should be able to identify these two clusters based on the similarity between words that appear in t he articles.
Table 1. Collection of news articles.
Article Words 1 dollar: 1, industry: 4, country: 2, loan: 3, deal: 2, government: 2 2 machinery: 2, labor: 3, market: 4, industry: 2, work: 3, country: 1 3 job: 5, inAat. Such observations are known as anomalies or outliers.
introduction data mining tan steinbach pdf
The goal of an anomaly detection al- gorithm is to discover the real anomalies and avoid falsely labeling normal objects as anomalous. In other words, a good anomaly detector must have a high detection rate and a low false alarm rate. Applications of anomaly detection include the detection of fraud, network intrusions, unusual patterns of disease, and ecosystem disturbances.
A credit card company records the transactions made by every credit card holder, along with personal information such as credit limit, age, annual income, and address. Since the number of fraudulent cases is relatively small compared to the number of legitimate transactions, anomaly detection techniques can be applied to build a profile of legitimate transactions for t he users. When a.
If the characteristics of the transaction are very different from the previously created profile, t hen the transaction is flagged as potentially fraudulent. A study of these principles and techniques is essential for developing a better understanding of how data mining technology can be applied to various kinds of data.
This book also serves as a starting point for readers who are interested in doing research in this fi eld.
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We begin t he technical discussion of this book with a. AILhough t. Chapter 3, on data exploration, discusses summary st. These techniques provide the means for quickly gaining insight into a data set.
It supplements the discussions in the other chapters with a discussion of the statistical concepts statistical significance, p-values, false discovery rate, permutation testing, etc. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis. The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data.
Classification: Some of the most significant improvements in the text have been in the two chapters on classification.
The introductory chapter uses the decision tree classifier for illustration, but the discussion on many topics—those that apply across all classification approaches—has been greatly expanded and clarified, including topics such as overfitting, underfitting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation.
Almost every section of the advanced classification chapter has been significantly updated.
The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. We have added a separate section on deep networks to address the current developments in this area.
Introduction to data mining pang ning tan vipin kumar pdf
The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved. Anomaly Detection: Anomaly detection has been greatly revised and expanded.
The reconstruction-based approach is illustrated using autoencoder networks that are part of the deep learning paradigm. Association Analysis: The changes in association analysis are more localized.Yes, fields 2 and 3 are basically the same, buL I assume that you probably noticed that.
The average yearly precipitation has less variability than the average monthly precipitation. A good clustering algorithm should be able to identify these two clusters based on the similarity between words that appear in t he articles. However , cou nt a ttributes, which are discrete, are also ratio attributes. Smith Stevens, the psychologist who originally defi ned t he t yp es of attributes shown in Table 2.
We begin with a definition of measure- ment and data collection errors and then consider a variety of problems that involve measurement error: Outliers can be legitimate data objects or values. If the categorical attribute is nominal, however, then other approaches are needed. For example, each record could be the purchase history of a customer, with a listing of items purchased at different times.
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