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Introduction To Data Mining Pang-ning Tan blogger.com - Free Download
@inproceedings{TanIntroductionTD, title={Introduction to Data Mining}, author={Pang-Ning Tan and Michael S. Steinbach and Vipin Kumar}, year={} } Pang-Ning Tan, Michael S. Steinbach, Vipin Kumar 1 Introduction What is Data Mining? Motivating Challenges The Origins of Data Mining. Introduction to Data Mining (First Edition) Pang-Ning Tan, Michigan State University, Provides both theoretical and practical coverage of all data mining topics. All files are in Adobe's PDF format and require Acrobat Reader. Resources for Instructors and Students: Link to PowerPoint Slides. [eBook - EnG] Introduction to Data Mining (P. N. Tan, M. Steinbach, V. Kumar - ) - Free ebook download as PDF File .pdf), Text File .txt) or view presentation slides online. Scribd is the world's largest social reading and publishing site/5(27).
Introduction to data mining tan pdf download
Boston S;m Fr. 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 exploring 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 collection 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 introduction to data mining tan pdf download 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?
In 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 introduction to data mining tan pdf download 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, introduction to data mining tan pdf download. 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, introduction to data mining tan pdf download, the modeling of biochemical pathways, and phylogenetics.
Data mining is the process of automatically discovering useful information in large data 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. 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 introduction to data mining tan pdf download 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 introduction to data mining tan pdf download 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. The process of knowledge discovery In databases KDO. The input dat,a can be stored in a variety of formats flat files, introduction to data mining tan pdf download, 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. 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. 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 introduction to data mining tan pdf download 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, introduction to data mining tan pdf download.
As the role of data mining introduction to data mining tan pdf download business, science, medicine, 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.
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 semi-structured 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 resources belonging to multiple entities. This requi res the development of distributed data mining techniques.
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. Unfortunately, this process is extremely labor- intensive.
Current data analysis tasks often require the generation and evalu- ation of thousands of hypotheses, and consequently, introduction to data mining tan pdf download, 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, 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, and learning theories from artificial intelligence, pattern recognition, introduction to data mining tan pdf download, 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. 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, introduction to data mining tan pdf download, 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. 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. Introduction to data mining tan pdf download goal of both tasks is to learn a model that introduction to data mining tan pdf download the error between the predicted and true values of the target variable, introduction to data mining tan pdf download.
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 introduction to data mining tan pdf download an Iris flower as to whether it belongs to one of the following three Iris species: Setosa, Versicolour, or Virginica. To per- form this task, we need a data set containing the characterist ics of various flowers of these three species.
A data set with this type of information is t he well-known Iris data set from the UCI Machine Learning Reposit ory at In addition to the species of a flower, this data set contains four other attributes: sepal width, sepal length, petal length, and petal width. The Iris data set and its attributes are described further in Section 3. Petal width is broken into the categories low, medium, and high, which correspond to the intervals [0, 0.
Also, petal length is broken into categories low, medium, and high, which correspond to the intervals [0, 2. Based on these categories of petal width and length, the following rules can be derived: Petal width low and petal length low implies Setosa.
Petal width medium and petal length medium implies Versicolour. Petal width high and petal length high implies Virginica. While these rules do not classify all the flowers, they do a good but not perfect job of classifying most of the flowers.
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[eBook - EnG] Introduction to Data Mining (P. N. Tan, M. Steinbach, V. Kumar - ) - Free ebook download as PDF File .pdf), Text File .txt) or view presentation slides online. Scribd is the world's largest social reading and publishing site/5(27). Tan, P., Steinbach, M., & Kumar, V. () “Introduction To Data Mining, 2nd Edition”,.pdf - Free download Ebook, Handbook, Textbook, User Guide PDF files on the. Introduction to Data Mining (First Edition) Pang-Ning Tan, Michigan State University, Provides both theoretical and practical coverage of all data mining topics. All files are in Adobe's PDF format and require Acrobat Reader. Resources for Instructors and Students: Link to PowerPoint Slides.
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