introduction to data mining course syllabus


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PDF CSE 5243-0050 – Spring 2021 Introduction to Data Mining

Course Description: Knowledge discovery data mining data preprocessing data transformations; clustering classification frequent pattern mining anomaly detection avoiding false discoveries graph and network analysis; applications Prerequisites: Introduction to Databases Introduction to Algorithms or grad standing or permission of

  • What is a data mining course?

    This course is an introductory course on data mining. It introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions: (1) pattern discovery and (2) cluster analysis.

  • What are the applications of data mining?

    A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.

  • Where can I find a book about data mining?

    Data Mining: Concepts and Techniques, 3rd ed. Jiawei Han, Micheline Kamber, and Jian Pei, Morgan Kaufmann Publishers, 2011, ISBN: 9780123814791. o Click the link above to access this book in full-text online from the Purdue Libraries.

  • What skills should a student learn in data mining?

    Students are expected to gain the skills to formulate data mining problems, solve the problems using data mining techniques and interpret the output. Data: Types of data, data quality, data preprocessing, measure of similarity and dissimilarity, data exploration and visualization

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