15IS51T: Data Mining Information Sci 5th Sem Syllabus for Diploma DTE Karnataka C15 Scheme

Data Mining detail DTE Kar Diploma syllabus for Information Science And Engineering (IS), C15 scheme is extracted from DTE Karnataka official website and presented for diploma students. The course code (15IS51T), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below. The syllabus PDFs can be downloaded from official website.

For all other information sci 5th sem syllabus for diploma c15 scheme dte karnataka you can visit Information Sci 5th Sem Syllabus for Diploma C15 Scheme DTE Karnataka Subjects. The detail syllabus for data mining is as follows.

Pre-requisites:

Knowledge ofweb programming language, Database.

Course Objectives:

1. Understanding data mining and guidelines.
2. Recognizing data pre-processing and outliers and knowing the techniques of displaying data graphically.
3. Implementation of decision tree approaches.
4. Gain knowledge about cluster analysis and techniques.
5. Recognizing web data mining.
6. Focus onnew emerging Technologies and Applications in data mining, Privacy legislation in India and technological solutions.

Course Outcomes:

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UNIT I introduction to Data mining 08 Hrs

What is data mining? Data mining process, application, techniques, practical examples of data mining, Guidelines for successful data mining, limitations of data mining,

UNIT II: Data understand, data preparation and classification 10 Hrs

Data collection and preprocessing, outliers, mining outliers, missing data, types of data, computing distance ,data summarizing using basic statistical measurement ,Displaying the data graphically, multidimensional data visualization .
Classification, Decision trees, decision tree rules, estimating predictive accuracy of classification methods, improving accuracy of classification methods, other evaluation criteria for classification methods ,classification software.

UNIT III: Cluster analysis 06 Hrs

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UNIT IV: Web data mining 12 Hrs

Introduction to Web mining, web terminology and characteristics, locality and hierarchy in
the web, web content mining, web usage mining ,web mining software.

UNIT V: Search Engine and Query mining 08 Hrs

Introduction ,Differences b/w web search and information retrieval, characteristics ,
functionalities ,architecture of search engine, Search query mining, Individual privacy and query data mining.

UNIT VI: Information Privacy and Data mining 08 Hrs

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Text Books:

1. Introduction to Data Mining with case studies.— G.K.Gupta PHI Publications.

Reference Books:

1. DataMining: Introductory and Advanced Topics- Author: Margaret H. Dunham ,Pearson.
2. Data mining concepts and techniques, –Jiawei Han and Micheline Kamber, second edition,Welly Publications.
3. www.tutorialspoint.com/data mining/data mining tutorial.pdf
4. www.dataminingbook.com
5. Any other books covering the contents of the paper in more depth.
6. Latest and additional good books may be suggested and added from time to time.

Note: The following activities or similar activities for assessing CIE (IA) for 5 marks (Any one)

Student activity

Student activity like mini-project, surveys, quizzes, etc. should be done in group of 1-2 students.

1. Each group should do any one of the following type activity or any other similar activity related to the course and before conduction, get it approved from concerned course coordinator and programme co-ordinator
2. Each group should conduct different activity and no repeating should occur.

1. Seminars on clusters, web data mining,
2. Debates on data mining.
3. Conduct a survey on various applications of data mining and submit a report of 3 to 4 pages.
4. Conduct a case study on privacy conflicts for online shops.

Course Delivery:

The course will be delivered through Lectures and Power point presentations/ Video

Model Question Paper:

(CIE)

1. List the applications of Data mining.
2. With neat diagram explain the CRISP-DM approach.
3. Explain extraction transformation and loading.
4. Write the various approaches of missing data to be handled in data mining.

Model Question Paper:

PART-A

Answer any SIX questions. Each carries 5 marks. 5X6=30 Marks

1. Discuss some of the reasons for growth in enterprise data.
2. Explain the problems of data mining.
3. Explain multivariate outliers.
4. Explain web usage mining. How can web pages be clustered? What is the STC algorithm?
5. Explain search engine query mining.
6. Explain privacy preserving data mining.
7. List and explain the goals of web search.
8. Explain aims of cluster analysis.

PART-B

Answer any SEVEN full questions each carries 10 marks. 10 X 7=70 Marks

1. Explain the guidelines for successful data mining. Why are these guidelines helpful?
2. Discuss the basic methods for displaying data graphically.
3. Explain the idea behind decision trees. How does it explain the structure for given set of data?
4. Explain the problem with the HITS algorithm.
5. Write the differences b/w web search and information retrieval.
6. List and explain basic OECD information privacy protection principles.
7. Give three reasons why the revised privacy principles of data mining.
8. Describe the concept of similar pages on the web. Give an algorithm for finding similar pages.
9. Explain the aims ofweb data mining.
10. Explain the concepts of Precision, Recall and Ranking and their relationships.

For detail syllabus of all other subjects of BE Information Sci, C15 scheme do visit Information Sci 5th Sem syllabus for C15 scheme.

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