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Exploratory Data Mining and Data Preparation

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  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