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introduction to data mining tan pdf free download
Introduction to data mining / PangNing Tan, Michael, "Introduction to Data Mining is a complete introduction to data mining for students, researchers, and professionals It provides a sound understanding of the foundations of data mining, in addition to covering many important advanced topics"BOOK JACKET. 24/7 Online Introduction to Data Mining, 2nd Edition. Introduction to Data Mining, 2nd Edition Preface is available for download in PDF format. Download Preface. Instructor Solutions Manual for Introduction to Data Mining, 2nd Edition Tan, Steinbach & Kumar © Format On-line Supplement ISBN Format: On-line Supplement 2. Suppose that you are employed as a data mining consultant for an In-ternet search engine company. Describe how data mining can help the company by giving specific examples of how techniques, such as clus-tering, classification, association rule mining, and anomaly detection can be applied. The following are examples of possible blogger.com Size: 1MB
Introduction to data mining tan steinbach kumar pdf download
Boston S;m Fr. mcisco New York London Toronto Sydney Tokyo Singapore Madrid Mexico Cicy Munich Paris Cape Town Hong Kong Montreal Contents Preface vii 1 Introduction 1 1. However, extracting useful information has proven extremely challenging. Often, traditional data analy- sis tools and techniques cannot be used because of the massive size of a data set. Sometimes, t he non-traditional nature of the data means that traditional approaches cannot be applied even if the data set is relatively small.
In other situations, the questions t hat need to be answered cannot be addressed using existing data analysis techniques, and thus, new methods need to be devel- oped. Data mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It has also opened up exciting opport unities for introduction to data mining tan steinbach kumar pdf download and analyzing new types of data and for analyzing old types of data in new ways.
In this introductory chapter, we present an overview of data mining and outline the key topics to be covered in this book. We start with a descript ion of some well-known applications that require new techniques for data analysis. Business Point-of-sale data introduction to data mining tan steinbach kumar pdf download bar code scanners, radio frequency identification RFIDand smart card technology have allowed retailers to collect up-to-the-minute data about customer purchases at the checkout coun- ters of their stores.
Retailers can utilize this information, along with other business-critical data such as Web logs from e-commerce Web sites and cus- tomer service records from call centers, to help them better understand the needs of their customers and make more informed business decisions. Data mining techniques can be used to support a wide range of business intelligence applications such as customer profiling, targeted marketing, work- flow management, store layoutand fraud detection.
It can also help retailers Medicine, Science, and Engineering Researchers in medicine, science, and engineering are rapidly accumulating data that is key to important new discoveries.
For example, as an important step toward improving our under- standing of the Earth's climate system, NASA has deployed a series of Earth- orbiting satellites that continuously generate global observations of the land sur face, oceans, and atmosphere.
However, because of the size and spatia- temporal nature of the data, tradit ional methods are often not suitable for analyzing these data sets. Techniques developed in data mining can aid Earth scientists in answering questions such as "What is the relationship between the frequency and intensity of ecosystem disturbances such as drougllts and hurricanes to global warming? Introduction to data mining tan steinbach kumar pdf download the past, traditional methods in molecu- lar biology allowed scientists to study only a few genes at a time in a given experiment.
Recent breakthroughs in microarray technology have enabled sci- entists to compare the behavior of thousands of genes under various situations. Such comparisons can help determine the function of each gene and perhaps isolate the genes responsible for certain diseases. However, the noisy and high- dimensional nature of data requires new types of data analysis. In addition to analyzing gene array data, data mining can also be used to address other important biological challenges such as protein structure prediction, multiple sequence alignment, the modeling of biochemical pathways, and phylogenetics.
Data mining is the process of automatically discovering useful information in large introduction to data mining tan steinbach kumar pdf download repositories. Data mining techniques are deployed to scour large databases in order to find novel and useful patterns that might otherwise remai n unknown.
They also provide capabili ties to predict t. he outcome of a 1. Not all information discovery tasks are considered to be data mining. For example, looking up individual records using a database management system or fi nding particular Web pages via a query to an Int ernet search engine are tasks related to the area of information r etrieval.
Although such tasks are important and may involve the use of the sophisticated algorithms and data struct ures, t hey rely on traditional computer science techniques and obvious feat ures of the data to create index structures for efficiently organizing and retrievi ng information. Nonetheless, data mining techniques have been used to enhance information retrieval systems. Data Mining and Knowledge Discovery Data mi ning is an integral part of knowledge discovery in databases KDDwhich is t he overall process of convert ing raw data into useful in- formati on, as shown in Figure 1.
This process consists of a series of trans- formation steps, from data preprocessi ng to postprocessing of data mining results. Input Data Feature Selection Dimensionality Reduction Normalization Data Subsetting Informat ion Filtering Patterns Visualization Pattern Interpretati on Figure 1.
The process of knowledge discovery In databases KDO. The input dat,a can be stored in a variety of formats flat files, spread- sheets, or relational tables and may reside in a centrali zed data repository or be dist,r ibuted across multiple sites. The purpose of pr eprocessing is to transform the raw input data into an appropriate format for subsequent analysis. The steps involved in data preprocessing include fusing data from multiple sources, cleaning data to remove noise and duplicate observations, and selecting records and features t hat are relevant to t he data mi ni ng task at hand.
Because of the many ways data can be collected and stored, data 4 Chapter 1 Introduction preprocessing is perhaps the most laborious and time-consuming step in the overall knowledge discovery process, introduction to data mining tan steinbach kumar pdf download.
For example, in business applications, the insights offered by data mining results can be integrated with campaign management tools so that effective marketing pro- motions can be conducted and tested. Such integration requires a postpro- cessing step that ensures that only valid and useful results are incorporated into the decision support system. An example of postprocessing is visualiza- tion see Chapter 3which allows analysts to explore the data and the data mining results from a variety of viewpoints.
Statistical measures or hypoth- esis testing methods can also be applied during postprocessing to eliminate spurious data mining results. The following are some of the specifi c challenges that motivated the develop- ment of data mining.
Scalability Because of advances in data generation and collection, data sets with sizes of gigabytes, terabytes, or even petabytes are becoming common. lf data mining algorithms are to handle these massive data sets, then they must be scalable. Many data mining algorithms employ special search strate- gies to handle exponential search problems.
Scalability may also require the implementation of novel data structures to access individual records in an ef- ficient manner. For instance, out-of-core algorithms may be necessary when processing data sets that cannot fit into main memory.
Scalability can also be improved by using sampling or developing parallel and distributed algorithms. High Dimensionality It is now common to encounter data sets with hun- dreds or thousands of attributes instead of the handful common a few decades ago. In bioinformatics, progress in microarray technology has produced gene expression data involving thousands of featur es. Data sets with temporal or spatial components also tend to have high dimensionality. For example, consider a data set that contains measurements of temperature at various locations.
If the temperature measurements are taken repeatedly for an ex- tended period, the number of dimensions features increases in proportion to 1.
Traditional data analysis techniques that were developed for low-dimensional data often do not work well for such high- dimensional data. Also, for some data analysis algorithms, the computational complexity increases rapidly as the dimensionality the number of features increases. Heterogeneous and Complex Dat a Traditional data analysis methods often deal with data sets containing attributes of the same type, either contin- uous or categorical.
As the role of data mining in business, science, medicine, introduction to data mining tan steinbach kumar pdf download, and other fields has grown, so has the need for techniques that can handle heterogeneous attributes. Recent years have also seen the emergence of more complex data objects. Examples of such non-traditional types of data include collections of Web pages containing semi-structured text and hyperlinks; DNA data with sequential and three-dimensional structure; and climate data that consists of time series measurements {temperature, pressure, etc, introduction to data mining tan steinbach kumar pdf download.
at various locations on the Earth's surface. Techniques developed for mining such com- plex objects should take into consideration relationships in the data, such as temporal and spatial autocorrelation, graph connectivity, and parent-child re- lationships between the elements in introduction to data mining tan steinbach kumar pdf download text and XML documenls.
Data Ownership and Distribut ion Sometimes, the data needed for an analysis is not stored in one location or owned by one organization. Instead, the data is geographically distributed among introduction to data mining tan steinbach kumar pdf download belonging to multiple entities. This requi res the development of distributed data mining techniques, introduction to data mining tan steinbach kumar pdf download.
Among the key challenges faced by distributed data mining algorithms in- clude 1 how to reduce the amount of communication needed t o perform the distributed computation, 2 how to effectively consolidate t he data minillg results obtained from multiple sources, and 3 how to address data security issues.
Non-trad itional Analysis The traditional statistical approach is based on a hypothesize-and-test paradigm. ln other words, a hypothesis is proposed, an experiment is designed to gather the data, and then the data is analyzed with respect to the hypothesis. Unfortunately, this process is extremely labor- intensive. Current data analysis tasks often require the generation and evalu- ation of thousands of hypotheses, and consequently, the development of some data mining techniques has been motivated by the desire to automate the process of hypothesis generation and evaluation.
Furthermore, the data sets analyzed in data mining are typically not the result of a carefully designed 6 Chapter 1 Introduction experiment and often represent opportunistic samples of the data, rat her than random samples. Also, t he data sets frequently involve non-traditional types of data and data distributions. This work, which culminated in the field of data mining, introduction to data mining tan steinbach kumar pdf download, built upon the methodology and algorithms that researchers had previously used.
In particular, data mining draws upon ideas, such as 1 sampling, estimation, and hypothesis testing from statistics and 2 search algorithms, modeling techniques, introduction to data mining tan steinbach kumar pdf download, and learning theories from artificial intelligence, pattern recognition, and machine learning. Data mining has also been quick to adopt ideas from other areas, including optimization, evolutionary computing, informat ion theory, signal processing, visualization, and information retrieval.
A number of other areas also play key supporting roles. In particular, database systems are needed to provide support for efficient. storage, index- ing, and query processing. Techniques from high performance parallel com- puting are often important in addressing the massive size of some data sets. Distributed techniques can also help address the issue of size and are essential when the data cannot be gathered in one location. Figure 1.
Data mining as a confluence of many discipli nes. The objective of these tasks is to predict the value of a par- ticular attribute based on the values of other attributes.
The attribute to be predicted is commonly known as the target or dependent vari- able, while the attributes used for making t he prediction are known as the explanatory or independent variables. Descriptive tasks.
Here, t he objective is to derive pat terns correlations, t rends, clusters, trajectories, and anomalies that summarize the un- derlying relationships in data. Descri ptive data mining tasks are often exploratory in nature a nd frequently require postprocessing techniques to validate and explain the results. his book. II 'd Four of the core data mining tasks. There are two types of predictive modeling tasks: classification, which is used for discrete target variables, and r egression, which is used for continuous target variables.
For example, predicting whether a Web user will make a purchase at an online bookstore is a classification task because the target variable is binary-valued. On the other hand, forecasting the future price of a stock is a regression task because pr ice is a continuous-valued attribute.
The goal of both tasks is to learn a model that minimizes the error between the predicted and true values of the target variable. Predictive modeling can be used to identify customers t hat will respond to a marketing campaign, predict disturbances in the Earth's ecosystem, or judge whether a patient has a particular disease based on the results of medical tests.
Example 1. Consider the task of predicting a species of flower based on the characteristics of the flower. In particular, consider classifying an Iris flower as to whether it belongs to one of the following three Iris species: Setosa, Versicolour, or Virginica.
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"Introduction to Data Mining is a complete introduction to data mining for students, researchers, and professionals. It provides a sound understanding of the foundations of data mining, in addition to covering many important advanced topics."--Jacket. This is the Instructors Solution Manual for the book Introduction to data mining tan steinbach kumar pdf download Start your free trial today and explore our endless blogger.com-Ning Tan, Michael Steinbach, Vipin KumarShare book pagesEnglishPDFNot available on the Perlego appPang-Ning Tan, Michael Steinbach, Vipin KumarBook detailsTable of contentsIntroduction to Data Mining presents fundamental concepts and algorithms Download Download PDF (application/pdf) ( MB) PowerPoint Slides. PowerPoint Slides Companion Website for Introduction to Data Mining Tan, Steinbach & Kumar © Format: Website ISBN Introduction to Data Mining. Tan, Steinbach & KumarAvailability: Available
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