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Introduction to Data Mining
Introduction 1. Discuss whether or not each of the following activities is a data mining task. (a) Dividing the customers of a company according to their gender. No. This is a simple database query. (b) Dividing the customers of a company according to their prof-itability. No. This is an accounting calculation, followed by the applica-tion of a File Size: 1MB Preface to the Second Edition Since the first edition, roughly 12 years ago, much has changed in the field of data analysis. The volume and variety of data being collected continues a comprehensive introduction to data mining and is designed to be accessi-ble and useful to students, instructors, researchers, and professionals. Areas Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 19 Full PDFs related to this paper. READ PAPER. Data Mining: Concepts and Techniques (2nd Edition) Solution Manual. Download. Data Mining: Concepts and Techniques (2nd Edition) Solution Manual. Ankit Chaudhary. Related blogger.comted Reading Time: 8 mins
Introduction to data mining 2nd edition pdf download
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Need an account? Click here to sign up. Download Free PDF. Data Mining: Concepts and Techniques 2nd Edition Solution Manual. Ankit Chaudhary. Download PDF Download Full PDF Package This paper. A short summary of this paper. Do not copy!
Do not distribute! Some of the exercises in Data Mining: Concepts and Techniques are themselves good research topics that may lead to future Master or Ph. Therefore, our solution manual was prepared as a teaching aid to be used with a grain of salt. It is also possible that the solutions may contain typos or errors. If you should notice any, please feel free to point them out by sending your suggestions to hanj cs.
We appreciate your suggestions. Chai, Meloney H. Chang, James W. Herdy, Jason W. Ma, Jiuhong Xu, Chunyan Yu, and Ying Zhou. They have all contributed substantially to the work on the solution manual of first edition of this book. For those questions that also appear in the first edition, the answers in this current solution manual are largely based on those worked out in the preparation of the first edition.
Second, we would like to thank two Ph. candidates, Deng Cai and Hector Gonzalez, who have served as assistants in the teaching of our data mining course: CS Introduction to Data Mining, in the Department of Computer Science, University of Illinois at Urbana-Champaign, in Fall They have helped preparing and compiling the answers for some of the exercise questions. Moreover, our thanks go to several students, introduction to data mining 2nd edition pdf download,whose answers to the class assignments have contributed to the improvements of this solution manual.
What is data mining? In your answer, address the following: a Is it another hype? b Is it a simple transformation of technology developed from databases, statistics, and machine learning? c Explain how the evolution of database technology led to data mining. d Describe the steps involved in data mining when viewed as a process of knowledge discovery.
a Is it another hype? Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge.
Thus, data mining can be viewed as the result of the natural evolution of information technology. Data mining is more than a simple transformation of technology developed from databases, statis- tics, and machine learning. Instead, data mining involves an introduction to data mining 2nd edition pdf download, rather than a simple transfor- mation, of techniques from multiple disciplines such as introduction to data mining 2nd edition pdf download technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information re- trieval, image and signal processing, and spatial data analysis.
Database technology began with the development of data collection and database creation mechanisms that led to the development of effective mechanisms for data management including data storage and retrieval, and query and transaction processing. The large number of database systems offering query and transaction processing eventually and naturally led to the need for data analysis and understanding.
Hence, data mining began its development out of this necessity. Present an example where data mining is crucial to the success of a business. What data mining functions does this business need?
Can they be performed alternatively by data query processing or simple statistical analysis? Answer: A department store, for example, can use data mining to assist with its target marketing mail campaign. Using data mining functions such as association, the store can use the mined strong association rules to determine which products bought by one group of customers are likely to lead to the buying of certain other products. With this information, the store can then mail marketing materials only to those kinds of customers who exhibit a high likelihood of purchasing additional products.
Data query processing is used for data or information retrieval and does not have the means for finding association rules.
Similarly, simple statistical analysis cannot handle large amounts of data such as those of customer records in a department store. Suppose your task as a software engineer at Big-University is to design a data mining system to examine their university course database, which contains the following information: the name, address, and status e.
Describe the architecture you introduction to data mining 2nd edition pdf download choose. What is the purpose of each component of this architecture? For example, the knowledge base may contain metadata which describes data from multiple heterogeneous sources.
How is a data warehouse different from a database? How are they similar? There could be multiple heterogeneous databases where the schema of one database may not agree with the schema of another.
A database system supports ad-hoc query and on-line transaction processing. Briefly describe the following advanced database systems and applications: object-relational databases, spatial databases, text databases, multimedia databases, the World Wide Web.
Each entity in the database is considered as an object. The object contains a set of variables that describe the object, a set of messages that the object can use to communicate with other objects or with the rest of the database system, and a set of methods where each method holds the code to implement a message.
Raster data consists of n-dimensional bit maps or pixel maps, and vector data are represented by lines, points, polygons or other kinds of processed primitives, Some examples of spatial databases include geographical map databases, VLSI chip designs, and medical and satellite images databases.
Some examples of distributed information services associated with the World-Wide Web include America Online, Yahoo! Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, prediction, clustering, and evolution analysis.
Give examples of each data mining functionality, using a real-life database that you are familiar with. For example, the characteristics of students can be produced, generating a profile of all the University first year computing science students, which may include such information as a high GPA and large number of courses taken.
Their similarity is that they are both tools for prediction: Classification is used for predicting the class label of data objects and prediction is typically used for predicting missing numerical data values.
The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Each cluster that is formed can be viewed as a class of objects. Clustering can also facilitate taxonomy formation, introduction to data mining 2nd edition pdf download, that is, the organization of observations into a hierarchy of classes that group similar events together.
Although this may include characterization, discrimination, association, classifi- cation, or clustering of time-related data, distinct features of such an analysis include time-series data analysis, sequence or periodicity pattern matching, and similarity-based data analysis. What is the difference between discrimination and classification? Between characterization and clustering? Between classification and prediction? For each of these pairs of tasks, how are they similar?
Discrimination and classification are similar in that they both deal with the analysis of class data objects. This pair of tasks is similar in that they both deal with grouping together objects or data that are related or have high similarity in comparison to one another.
This pair of tasks is similar in that they both are tools for prediction: Introduction to data mining 2nd edition pdf download is used for predicting the class label of data objects and prediction is typically used for predicting missing numerical data values. Based on your observation, describe another possible kind of knowledge that needs to be discovered by data mining methods but has not been listed in this chapter.
Does it require a mining methodology that is quite different from those outlined in this chapter? EXERCISES 7 There is no standard answer for this question and one can judge the quality of an answer based on the freshness and quality of the proposal.
For example, one may propose partial periodicity as a new kind of knowledge, introduction to data mining 2nd edition pdf download, where a pattern is partial periodic if only some offsets of a certain time period in a time series demonstrate some repeating behavior.
List and describe the five primitives for specifying a data mining task. It involves specifying the database and tables or data warehouse containing the relevant data, conditions for selecting the relevant data, the relevant attributes or dimensions for exploration, and instructions regarding the ordering or grouping of the data retrieved. As well, the user can be more specific and provide pattern templates that all discovered patterns must match. These templates, or metapatterns also called metarules or metaqueriescan be used to guide the discovery process.
Such knowledge can be used to guide the knowledge discovery process and evaluate the patterns that are found. Of the several kinds of background knowledge, this chapter focuses on concept hierarchies. This allows the user to confine the number of uninteresting patterns returned by the process, as a data mining process may generate a large number of patterns.
Interestingness measures can be specified introduction to data mining 2nd edition pdf download such pattern characteristics as simplicity, certainty, utility and novelty. In order for data mining to be effective in conveying knowledge to users, data mining systems should be able to display the discovered patterns in multiple forms such as rules, tables, cross tabs cross-tabulationspie or bar charts, decision trees, cubes or other visual representations.
Describe why concept hierarchies are useful in data mining. Answer: Concept hierarchies define a sequence of mappings from a set of lower-level concepts to higher-level, more general concepts and can be represented as a set of nodes organized in a tree, in the form of a lattice, or as a partial order.
They are useful in data mining because they allow the discovery of knowledge at multiple levels of abstraction and provide the structure on which data can be generalized rolled-up or specialized drilled-down. Together, these operations allow users to view the data from different perspectives, gaining further insight into relationships hidden in the data. This will be more efficient than mining on a large, uncompressed data set.
Outliers are often discarded as noise.
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Description. For courses in data mining and database systems. Introducing the fundamental concepts and algorithms of data mining. Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and blogger.comted in a clear and accessible way, the book Format: On-line Supplement Introduction 1. Discuss whether or not each of the following activities is a data mining task. (a) Dividing the customers of a company according to their gender. No. This is a simple database query. (b) Dividing the customers of a company according to their prof-itability. No. This is an accounting calculation, followed by the applica-tion of a File Size: 1MB Preface to the Second Edition Since the first edition, roughly 12 years ago, much has changed in the field of data analysis. The volume and variety of data being collected continues a comprehensive introduction to data mining and is designed to be accessi-ble and useful to students, instructors, researchers, and professionals. Areas
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