SRKIT/7.5/1/RC 04

TEACHING PLAN CUM REALIZATION

Department : CSE Name of faculty: S SURESH BABU Designation: Assistant Professor

Semester / Year: III/II Name of the subject: Data warehousing and Data Mining

Sl

No

Unit / Topic Teaching Planned on

( Date)

Taught on (Date)

No of Periods

( Planned)

No of Periods

(actual taken)

Remarks

(if any deviation)

1 Unit:1 Introduction: 21-11-2016 1

2 What motivated Data Mining? Why it is important.

24-11-2016 1

3 Data Mining-on what kind of data? 25-11-2016, 1

4 Data Mining Functionalities26-11-2016 1

5 What Kinds of Patterns can be Mined? 28-11-2016 1

Prepared: Faculty / Date Verified: HOD/Date

6Are all of the patterns Interesting

1-12-2016 1

7 Classification of data mining systems2-12-2016 1

8 Data Mining Task Primitives 3-12-2016 1

9Integration of a Data Mining System with a database

5-12-2016 1

10 Tutorial 7-12-2016 1

11Data ware house systems 8-12-2016 1

12Major issues in Data mining 9-12-2016 1

13Unit:2 Data Pre-Processing 10-12-2016 1

14 Why pre-process the data? 12-12-2016 1

15 Descriptive Data Summarization 15-12-2016 1

16 Data Cleaning 16-12-2016 1

17 Data Integration and Transformation 17-12-2016 1

18 Data Reduction 19-12-2016 1

19 Tutorial 21-12-2016

Prepared: Faculty / Date Verified: HOD/Date

20 Data Discretization 22-12-2016 1

21 Concept Hierarchy Generation 23-12-2016 1

22Unit:3 Data Ware house and OLAP Technology

24-12-2016 1

23An overview: what is a data warehouse?

26-12-2016 1

24 A multi-dimensional data model 29-12-2016 1

25 Data warehouse architecture 30-12-2016 1

26 Data warehouse implementation 31-12-2016 1

27 From data warehousing to data mining 2-1-2017 1

28 Tutorial 4-1-2017 1

29 Unit: 4 Classification 5-1-2017 1

30 Basic concepts 6-1-2017 1

31General approach to solving a classification problem

7-1-2017 1

32 Decision tree induction 9-1-2017 1

Prepared: Faculty / Date Verified: HOD/Date

33 Working of decision tree 23-1-2017 1

34 Building a decision tree 27-1-2017 1

35Methods for expressing an attribute test conditions

28-1-2017 1

36 Measures for selecting the best split 30-1-2017 1

37 Algorithm for decision tree induction 31-1-2017 1

38

Model over fitting: due to presence of noise

2-2-2017 1

39 Due to lack of representation samples 3-2-2017 1

40Evaluating the performance of classifier

4-2-2017 1

41 Hold out method 6-2-2017 1

42 Tutorial 8-2-2017 1

43 Random sub sampling 9-2-2017 1

44 Cross validation 10-2-2017 1

45 Bootstrap 11-2-2017 1

Prepared: Faculty / Date Verified: HOD/Date

46 Unit: 5 Association analysis 13-2-2017 1

47 Basic concepts and algorithms 16-2-2017 1

48Introduction, Frequent item set generation

17-2-2017 1

49 Rule generation 18-2-2017 1

50Compact representation of frequent item sets

20-2-2017 1

51 Tutorial 22-2-2017 1

52 FP-growth algorithm 23-2-2017 1

53Unit:6 Cluster analysis: basic concepts and algorithms

25-2-2017 1

54 What is cluster analysis? 27-2-2017 1

55 Different types of clustering 2-3-2017 1

56 Different types of clusters 3-3-2017 1

57K-means ,The basic K-means algorithm

4-3-2017 1

58 K-means: Additional issues, Bisection 6-3-2017 1

Prepared: Faculty / Date Verified: HOD/Date

k-means

59k-means and different types of clusters , strengths and weaknesses

9-3-2017 1

60 K-means as an optimization problem 10-3-2017 1

61 Agglomerative hierarchical clustering 11-3-2017 1

62

Basic agglomerative hierarchical clustering algorithm, specific techniques

13-3-2017 1

63

DBSCAN, Traditional Density: Center-based approach, the DBSCAN algorithm, strengths and weaknesses

16-3-2017 1

64 Tutorial 17-3-2017 1

65 Revision 18-3-2017 1

Prepared: Faculty / Date Verified: HOD/Date