![[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SUSHIL KULKARNI](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Ch 1 Intro to Data Mining
Similar to Ch 1 Intro to Data Mining (20)
Recently uploaded
Recently uploaded (20)
Ch 1 Intro to Data Mining
- 1. SUSHIL KULKARNI INTRODUCTION TO DATA MINING
- 7. DATA MINING DEFINITION SUSHIL KULKARNI
- 19. DATA MINING PROCESS SUSHIL KULKARNI
- 24. DATA MINNING QUERIES SUSHIL KULKARNI
- 28. KDD PROCESS SUSHIL KULKARNI
- 33. VISUALIZATION TECHNIQUES Hybrid- combination of above approaches Hierarchical- Hierarchically dividing display area Pixel-based- data as colored pixels Icon-based- using colors figures as icons Geometric- boxplot, scatter plot Graphical -bar charts,pie charts histograms
- 40. DATA MINING TASKS AND METHODS SUSHIL KULKARNI
- 45. Data Mining Predictive Descriptive Classification Regression Time series Analysis Prediction Clustering Summarization Association rules Sequence Discovery SUSHIL KULKARNI
- 49. DATA PREPROCESSING SUSHIL KULKARNI
- 56. TASKS IN DATA PREPROCESSING SUSHIL KULKARNI
- 59. Forms of data preprocessing SUSHIL KULKARNI
- 60. DATA CLEANING SUSHIL KULKARNI
- 64. HOW TO HANDLE MISSING DATA? Fill missing values using aggregate functions (e.g., average) or probabilistic estimates on global value distribution E.g., put the average income here, or put the most probable income based on the fact that the person is 39 years old E.g., put the most frequent team here SUSHIL KULKARNI F ? 45,390 45 F Yankees ? 39 M Red Sox 24,200 23 Gender Team Income Age
- 74. SIMPLE DISCRETISATION METHODS: BINNING Example: customer ages 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 Equi-width binning: number of values 0-22 22-31 44-48 32-38 38-44 48-55 55-62 62-80 Equi-depth binning: SUSHIL KULKARNI
- 75. FEW TASKS SUSHIL KULKARNI
- 78. CLUSTER ANALYSIS cluster outlier salary age
- 81. REGRESSION x y y = x + 1 X1 Y1 (salary) (age) Example of linear regression SUSHIL KULKARNI
- 82. DATA INTEGRATION SUSHIL KULKARNI
- 85. DATA TRANSFORMATION SUSHIL KULKARNI
- 91. DATA EXTRACTION, SELECTION, CONSTRUCTION, COMPRESSION SUSHIL KULKARNI
- 94. SELECTION: DECISION TREE INDUCTION: Example Initial attribute set: {A1, A2, A3, A4, A5, A6} A4 ? A1? A6? Class 1 Class 2 Class 2 Reduced attribute set: {A1, A4, A6} Class 1 > SUSHIL KULKARNI
- 97. DATA COMPRESSION Original Data Compressed Data lossless Original Data Approximated lossy SUSHIL KULKARNI
- 109. SAMPLING SRSWOR (simple random sample without replacement) SRSWR Raw Data SUSHIL KULKARNI
- 115. VIEW DATA USING DATA MINING SUSHIL KULKARNI
- 123. FUZZY SETS SUSHIL KULKARNI
- 124. FUZZY SETS Fuzzy set shows the triangular view of set of member ship values are shown in fuzzy set There is gradual decrease in the set of values of short, gradual increase and decrease in the set of values of median and, gradual increase in the set of values of tall. SUSHIL KULKARNI
- 125. CLASSIFICATION/ PREDICTION IS FUZZY Loan Amnt Simple Fuzzy Accept Accept Reject Reject SUSHIL KULKARNI
- 128. IR QUERY RESULT MEASURES AND CLASSIFICATION IR Classification SUSHIL KULKARNI
- 131. AGGREGATION HIERARCHIES SUSHIL KULKARNI
- 137. T H A N K S ! SUSHIL KULKARNI