Chapter Two

1. Chapter Two Principles of data mining

2. Chapter Overview • The process of data mining • Approaches of data mining • Categories of data mining problems • Information patterns to be discovered • Overview of data mining solutions • Importance of evaluation • Undertaking a data mining task in Weka • Review of basic concepts in statistics and probability

3. Data Mining Process Input Data Preparing Input Data Mining Patterns A data mining stage Post-processing Patterns Output Patterns Flow of control from one stage to the next stage Flow of control from one stage to the previous stage Repetition of the tasks at one stage

4. Data Mining Process Target Data set Collected Data set • Integrating data • Getting necessary data details Original Data sets • Preparation • Selecting relevant features • Selecting relevant records • Data cleaning • Deal with unknown data • Data transformation Pre-Processed Data set Formatted Data set • Formatting data into acceptable form by the mining tool

5. Data Mining Process • Mining • Determining data mining tasks • Assigning roles for data for certain tasks • Selecting data mining solution(s) to each task • Setting necessary parameters for the solution • Collecting result patterns Formatted Data set Solution3 (w1, w2, …, wm) Solution2 (t1, t2, …, tr) Parameter settings Solution1 (p1, p2, …, pn) Mining solutions Patterns

6. Data Mining Process Knowledge learnt Pattern Interpretation Evaluation criteria Selection criteria Valid Patterns Patterns accept Valid Patterns Selected Patterns reject • Post-processing • Pattern evaluation • Pattern selection • Pattern interpretation

7. Data Mining Process • Roles of participants in data mining • Participants include: • Data miners / data analysts: main participant of a DM project • Domain expert: main collaborators of DM project • Decision makers: clients of a DM project • Risk of human bias in the discovery process • Important roles of domain expert • Pattern interpretation (for usefulness) • Pattern evaluation (for significance) • Mining options (for suitable tasks, limited) • Advisory on data pre-processing (for suitable operations, limited) • Balancing the strength of human and machine

8. Data Mining Approaches • Hypothesis testing approach • Top-down lead by a hypothesis statement • Procedure: • Forming a hypothesis statement • Collecting and selecting data of relevance • Conducting data analysis and collecting patterns • Interpreting the patterns to accept/reject the hypothesis • Discovery approach • Bottom-up without a hypothesis in mind • Procedure: • Collecting and preparing data of interest • Conducting data analysis and discovering possible patterns • Evaluating the importance and interestingness

9. Data Mining Approaches • Discovery approach (cont’d) • Directed discovery (supervised learning): • Certain aspects of the outcome, i.e. the goal, of the discovery have been specified. The discovery is to find those patterns satisfying the goal. • e.g. patterns relating to the outcome of a class variable • Undirected discovery (unsupervised learning): • There is no specification of the goal of the discovery. The discovery is to find those patterns of some kind of significance. • e.g. associative links among some attribute values

10. Data Mining: Problems & Patterns • Classification • Construct a classification model to determine the class of a given record Model Construction Method Classification Model Example Data Set (a) Model Development Phase Unseen Data Record with undetermined class Data Record with the determined class Classification Model (b) Model Use Phase

11. Data Mining: Problems & Patterns • Various forms of classification models Neural network Decision tree Instance space Many more … List of ordered classification rules Function (linear regression)

12. Input data points Data Mining: Problems & Patterns • Cluster detection • Measure similarity among data objects and group them into clusters accordingly Clustering Method Cluster Memberships of Data Points

13. Data Mining: Problems & Patterns • Forms of clustering results Clusters of various shapes Hierarchical clustering results Eclipse shaped clusters

14. Data Mining: Problems & Patterns • Association rule mining • Discover significant relationships between data objects Association Mining Method X Y • Various associations • Between values, e.g.Apple  Coke • Between categories of values, e.g.Food  Magazine • Between values of attributes, e.g.Married:yes  OwnHouse:yes • Over time period, e.g. year 1: Database  year 2: Data Mining

15. Data Mining: Problems & Patterns • An example Classification model? Clusters? Association rules?

16. Data Mining Solutions: An Overview • Classification solutions • Decision tree e.g. ID3 • k nearest neighbour (kNN) e.g. PEBLS • Rules e.g. Sequential Cover • Bayesian theorem e.g. Naïve Bayes • Artificial neural network • Clustering Solutions • Partition-based methods e.g. K-means • Hierarchical methods e.g. agglomeration • Density-based methods e.g. DBScan • Model-based methods e.g. Expectation-Maximisation • Graph-based methods e.g. Chameleon

17. Data Mining Solutions: An Overview • Association rule solutions • Greedy methods e.g. Apriori • Graph-based methods e.g. FP-Growth • Methods for various associations • Boolean associations • Generalised associations (multi-level associations) • Quantitative associations (multidimensional associations) • Sequential associations (sequential patterns) Since one type of data mining problems can be transformed to another type of data mining problems, some solutions for one type can also be applied to another type.

18. Evaluation of Patterns • Importance of evaluating result patterns • Classification model must be accurate enough to be creditable • Clusters must genuinely exist • Association rules must have enough strengths to be believed • Data descriptions must be general enough to cover a large part of the data set How do we evaluate the discovered patterns ?

19. Evaluation of Patterns • Possible measures of interestingness • Objective measures based on data and pattern • Conciseness of pattern, e.g. minimum description length • Coverage, e.g. coverage for classification rules • Reliability, e.g. accuracy of a classification model • Peculiarity, e.g. measures of difference from the norm • Diversity, e.g. tendency of clusters • Subjective measures based on domain knowledge • Novelty • Surprisingness • Usefulness • Applicability

20. Evaluation of Patterns • Commonly used measures • Accuracy rate or error rate for classification models • True positive • False positive • False negative (see section 6.5.1) • Quality of clusters • Quality of a cluster • Overall quality of all clusters (see section 4.5.1) • Strengths of associations • Support • Confidence • Lift (see section 8.1.2 and 8.6)

21. Data Mining in Weka Explorer Associate Tab page • The roadmap Preprocess Tab page Tree Visualiser window Cluster Tab page Classify Tab page (3) (1) (2)

22. Generate random data set Display & edit data Save data set into a file Selected attribute summary Open data set from different sources Attribute display, selection & removal from the opened data set Visualise all attributes Selected attribute visualisation Data Mining in Weka Explorer • Preprocess Filters for pre-processing Data summary Feedback messages

23. Data Mining in Weka Explorer • Classify (as an example) Method selection & parameter setting Test option setting Result display window Task list. Menu of options available with right click.

24. Method List Selecting a specific method Data Mining in Weka Explorer • Classify (as an example) Selecting & Changing parameters

25. Data Mining in Weka Explorer Scatter plot of data object of different classes • Visualisation An Example Decision Tree

26. Probability & Statistics: A Brief Review • Where probability and statistics used? • Patterns found from data are probabilistic in nature • Used in various measures of evaluation, e.g. confidence measure of association rules • Used in data exploration stage for better understanding, e.g. maximum, minimum, mean, variance, skewness • Used during the mining process to assist the discovery of patterns, e.g. information gain for decision tree induction • Used as a part of patterns, e.g. naïve Bayes, Gaussian mixture model • Used in comparison of patterns, e.g. classification model with significantly better accuracy

27. Probability & Statistics: A Brief Review • Probability and conditional probability • Probability of event P(E) and its meanings when: • P(E) = 0, P(E) = 1 and 0 < P(E) < 1 • Probabilities of multiple events: • P(E and F), P(E or F) = P(E) + P(F) – P(E and F) • Mutually exclusive events: • P(E and F) = 0 and P(E and F) = P(E) + P(F) • Conditional probability of event E given event F: • P(E|F) = P(E and F)/P(F) • Independent events: • P(E and F) = P(E)P(F), and P(E|F) = P(E)

28. Probability & Statistics: A Brief Review • Probability & conditional probability (example)

29. Probability & Statistics: A Brief Review • Probability distribution of random variables • Discrete random variable • Continuous random variable 68% 95% P(X = x) P(a  X < b)

30. Probability & Statistics: A Brief Review • Basic Statistics • Sample mean, median and mode • Variance and standard deviation • Skewness

31. Probability & Statistics: A Brief Review Confidence interval estimate Sample mean is only an estimate of the true mean for the data population. Central limit theorem: sample means follows a normal distribution that: The mean is the true population mean X The standard deviation is Based on the central limit theorem and using the sample standard deviation to replace the true one, the following expression is used to estimate the interval for the true mean at confidence level of 1- 

32. Probability & Statistics: A Brief Review Confidence interval estimate (example) For this data set, n = 12, age = 26 and sage = 7.324. At confidence level of 95%, i.e. 1 -  = 0.95 and /2 = 0.025, n – 1 = 11, and therefore, t = 2.201. The interval estimate is: The interval is estimated as [21.347, 30.653] at confidence level of 95%

33. Probability & Statistics: A Brief Review Hypothesis testing As an introduction to statistical inference and statistic significance. Procedure: Forming null and alternative hypotheses Deciding the level of significance p Determining a test statistic and calculating its value Comparing the calculated value against known value and deciding if the null hypothesis should be rejected

34. Hypothesis testing (example) Assuming age = 25 Hypotheses: Null: Alternative: Calculating the statistic t as: Probability & Statistics: A Brief Review Less than t = 2.201 for p/2 = 0.025 and n – 1 = 11. • Conclusion: null hypothesis is not rejected, i.e. the difference between the sample mean and the population mean is insignificant.

35. Chapter Summary The data mining process involves preparation of data, mining of patterns and post-processing of the patterns. Top-down and bottom-up approaches are both useful. The discovery approach can be directed or undirected. Three main streams of data mining tasks and various forms of patterns and models are introduced. Specific solutions are required for specific types of problems The importance of evaluation of patterns must be appreciated. Normal procedure of conducting data mining in Weka is explained Some important basic concepts in probability and statistics are reviewed.

36. References Read Chapter 2 of Data Mining Techniques and Applications Useful further references Han, J. and Kamber, M. (2006), Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann Publishers, Chapter 1 Berry, M. J. A. and Linoff, G. (2004), Data Mining Techniques: For Marketing, Sales and Customer Relationship Management, 2nd ed. Wiley Computer Publishing, Chapters 1 – 2