Exploratory Data Mining and Data Preparation

1. Exploratory Data Mining and Data Preparation Data Mining

2. Data evaluation Data preparation Modeling Evaluation The Data Mining Process Business understanding Data Deployment Data Mining

3. Exploratory Data Mining • Preliminary process • Data summaries • Attribute means • Attribute variation • Attribute relationships • Visualization Data Mining

4. Summary Statistics • Possible Problems: • Many missing values (16%) • No examples of one value Appears to be a good predictor of the class Visualization Select an attribute Data Mining

5. Data Mining

6. Exploratory DM Process • For each attribute: • Look at data summaries • Identify potential problems and decide if an action needs to be taken (may require collecting more data) • Visualize the distribution • Identify potential problems (e.g., one dominant attribute value, even distribution, etc.) • Evaluate usefulness of attributes Data Mining

7. Weka Filters • Weka has many filters that are helpful in preprocessing the data • Attribute filters • Add, remove, or transform attributes • Instance filters • Add, remove, or transform instances • Process • Choose for drop-down menu • Edit parameters (if any) • Apply Data Mining

8. Data Preprocessing • Data cleaning • Missing values, noisy or inconsistent data • Data integration/transformation • Data reduction • Dimensionality reduction, data compression, numerosity reduction • Discretization Data Mining

9. Data Cleaning • Missing values • Weka reports % of missing values • Can use filter called ReplaceMissingValues • Noisy data • Due to uncertainty or errors • Weka reports unique values • Useful filters include • RemoveMisclassified • MergeTwoValues Data Mining

10. Data Transformation • Why transform data? • Combine attributes. For example, the ratio of two attributes might be more useful than keeping them separate • Normalizing data. Having attributes on the same approximate scale helps many data mining algorithms(hence better models) • Simplifying data. For example, working with discrete data is often more intuitive and helps the algorithms(hence better models) Data Mining

11. Weka Filters • The data transformation filters in Weka include: • Add • AddExpression • MakeIndicator • NumericTransform • Normalize • Standardize Data Mining

12. Discretization • Discretization reduces the number of values for a continuous attribute • Why? • Some methods can only use nominal data • E.g., in Weka ID3 and Apriori algorithms • Helpful if data needs to be sorted frequently (e.g., when constructing a decision tree) Data Mining

13. Unsupervised Discretization • Unsupervised - does not account for classes • Equal-interval binning • Equal-frequency binning Data Mining

14. 1 yes 8 yes & 5 no 9 yes & 4 no 1 no F E D C B A Supervised Discretization • Take classification into account • Use “entropy” to measure information gain • Goal: Discretizise into 'pure' intervals • Usually no way to get completely pure intervals: 64 65 68 69 70 71 72 75 80 81 83 85 Yes No Yes Yes Yes No No Yes No Yes Yes No Yes Yes Data Mining

15. Error-Based Discretization • Count number of misclassifications • Majority class determines prediction • Count instances that are different • Must restrict number of classes. • Complexity • Brute-force: exponential time • Dynamic programming: linear time • Downside: cannot generate adjacent intervals with same label Data Mining

16. Weka Filter Data Mining

17. Attribute Selection • Before inducing a model we almost always do input engineering • The most useful part of this is attribute selection (also called feature selection) • Select relevant attributes • Remove redundant and/or irrelevant attributes • Why? Data Mining

18. Reasons for Attribute Selection • Simpler model • More transparent • Easier to interpret • Faster model induction • What about overall time? • Structural knowledge • Knowing which attributes are important may be inherently important to the application • What about the accuracy? Data Mining

19. Attribute Selection Methods Data Mining

20. Filters • Results in either • Ranked list of attributes • Typical when each attribute is evaluated individually • Must select how many to keep • A selected subset of attributes • Forward selection • Best first • Random search such as genetic algorithm Data Mining

21. Filter Evaluation Examples • Information Gain • Gain ration • Relief • Correlation • High correlation with class attribute • Low correlation with other attributes Data Mining

22. Wrappers • “Wrap around” the learning algorithm • Must therefore always evaluate subsets • Return the best subset of attributes • Apply for each learning algorithm • Use same search methods as before Select a subset of attributes Induce learning algorithm on this subset Evaluate the resulting model (e.g., accuracy) Stop? No Yes Data Mining

23. How does it help? • Naïve Bayes • Instance-based learning • Decision tree induction Data Mining

24. Data Mining

25. Scalability • Data mining uses mostly well developed techniques (AI, statistics, optimization) • Key difference: very large databases • How to deal with scalability problems? • Scalability: the capability of handling increased load in a way that does not effect the performance adversely Data Mining

26. Massive Datasets • Very large data sets (millions+ of instances, hundreds+ of attributes) • Scalability in space and time • Data set cannot be kept in memory • E.g., processing one instance at a time • Learning time very long • How does the time depend on the input? • Number of attributes, number of instances Data Mining

27. Two Approaches • Increased computational power • Only works if algorithms can be sped up • Must have the computing availability • Adapt algorithms • Automatically scale-down the problem so that it is always approximately the same difficulty Data Mining

28. Computational Complexity • We want to design algorithms with good computational complexity exponential Time polynomial linear logarithm Number of instances (Number of attributes) Data Mining

29. Example: Big-Oh Notation • Define • n =number of instances • m =number of attributes • Going once through all the instances has complexity O(n) • Examples • Polynomial complexity: O(mn2) • Linear complexity: O(m+n) • Exponential complexity: O(2n) Data Mining

30. Classification • If no polynomial time algorithm exists to solve a problem it is called NP-complete • Finding the optimal decision tree is an example of a NP-complete problem • However, ID3 and C4.5 are polynomial time algorithms • Heuristic algorithms to construct solutions to a difficult problem • “Efficient” from a computational complexity standpoint but still have a scalability problem Data Mining

31. Decision Tree Algorithms • Traditional decision tree algorithms assume training set kept in memory • Swapping in and out of main and cache memory expensive • Solution: • Partition data into subsets • Build a classifier on each subset • Combine classifiers • Not as accurate as a single classifier Data Mining

32. Other Classification Examples • Instance-Based Learning • Goes through instances one at a time • Compares with new instance • Polynomial complexity O(mn) • Response time may be slow, however • Naïve Bayes • Polynomial complexity • Stores a very large model Data Mining

33. Data Reduction • Another way is to reduce the size of the data before applying a learning algorithm (preprocessing) • Some strategies • Dimensionality reduction • Data compression • Numerosity reduction Data Mining

34. Dimensionality Reduction • Remove irrelevant, weakly relevant, and redundant attributes • Attribute selection • Many methods available • E.g., forward selection, backwards elimination, genetic algorithm search • Often much smaller problem • Often little degeneration in predictive performance or even better performance Data Mining

35. Data Compression • Also aim for dimensionality reduction • Transform the data into a smaller space • Principle Component Analysis • Normalize data • Compute c orthonormal vectors, or principle components, that provide a basis for normalized data • Sort according to decreasing significance • Eliminate the weaker components Data Mining

36. PCA: Example Data Mining

37. Numerosity Reduction • Replace data with an alternative, smaller data representation • Histogram 1,1,5,5,5,5,5,8,8,10,10,10,10,12,14,14,14,15,15,15, 15,15,15,18,18,18,18,18,18,18,18,20,20,20,20,20, 20,20,21,21,21,21,25,25,25,25,25,28,28,30,30,30 count 1-10 11-20 21-30 Data Mining

38. Other Numerosity Reduction • Clustering • Data objects (instance) that are in the same cluster can be treated as the same instance • Must use a scalable clustering algorithm • Sampling • Randomly select a subset of the instances to be used Data Mining

39. Sampling Techniques • Different samples • Sample without replacement • Sample with replacement • Cluster sample • Stratified sample • Complexity of sampling actually sublinear, that is, the complexity is O(s) where s is the number of samples and s<<n Data Mining

40. Weka Filters • PrincipalComponents is under the Attribute Selection tab • Already talked about filters to discretize the data • The Resample filter randomly samples a given percentage of the data • If you specify the same seed, you’ll get the same sample again Data Mining