Data Warehousing and Data Mining Detailed Syllabus for Computer Science and Engineering M.Tech first year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for Data Warehousing and Data Mining M.Tech 2017-2018 (R17) first year second sem is as follows.
M.Tech. I Year II Sem.
Course Objectives:
- To develop the abilities of critical analysis to data mining systems and applications.
- To implement practical and theoretical understanding of the technologies for data mining
- To understand the strengths and limitations of various data mining models;
UNIT- I : Data mining Overview and Advanced Pattern Mining: Data mining tasks – mining frequent patterns, associations and correlations, classification and regression for predictive analysis, cluster analysis , outlier analysis; advanced pattern mining in multilevel, multidimensional space – mining multilevel associations, mining multidimensional associations, mining quantitative association rules, mining rare patterns and negative patterns.
UNIT- II : Advance Classification: Classification by back propagation, support vector machines, classification using frequent patterns, other classification methods – genetic algorithms, roughest approach, fuzz>set approach;
UNIT- III : Advance Clustering: Density – based methods –DBSCAN, OPTICS, DENCLUE; Grid-Based methods – STING, CLIQUE; Exception – maximization algorithm; clustering High- Dimensional Data; Clustering Graph and Network Data.
UNIT- IV : Web and Text Mining: Introduction, web mining, web content mining, web structure mining, we usage mining, Text mining – unstructured text, episode rule discovery for texts, hierarchy of categories, text clustering.
UNIT- V : Temporal and Spatial Data Mining: Introduction; Temporal Data Mining – Temporal Association Rules, Sequence Mining, GSP algorithm, SPADE, SPIRIT Episode Discovery, Time Series Analysis, Spatial Mining – Spatial Mining Tasks, Spatial Clustering. Data Mining Applications.
TEXT BOOKS:
- Data Mining Concepts and Techniques, Jiawei Hang Micheline Kamber, Jian pei, Morgan Kaufmannn.
- Data Mining Techniques – Arun K pujari, Universities Press.
REFERENCE BOOKS:
- Introduction to Data Mining – Pang-Ning Tan, Vipin kumar, Michael Steinbach, Pearson.
- Data Mining Principles & Applications – T.V Sveresh Kumar, B.Esware Reddy, Jagadish S Kalimani, Elsevier.
For all other M.Tech 1st Year 2nd Sem syllabus go to JNTUH M.Tech Computer Science and Engineering 1st Year 2nd Sem Course Structure for (R17) Batch.
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