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Principles of data mining

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1 Principles of data mining
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 Preparing Data Input Data Mining Patterns
Post-processing Input Data Output A data mining stage 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 Preparation Selecting relevant features
Selecting relevant records Data cleaning Deal with unknown data Data transformation Original Data sets Collected Data set Integrating data Getting necessary data details Target Data set 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 Post-processing Pattern evaluation
Pattern selection Pattern interpretation Patterns Evaluation criteria reject Valid Selection Selected accept Pattern Interpretation Knowledge learnt

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 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 Instance space Neural network Decision tree Many more … List of ordered classification rules Function (linear regression)

12 Data Mining: Problems & Patterns
Cluster detection Measure similarity among data objects and group them into clusters accordingly Input data points 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 and 8.6)

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

22 Data Mining in Weka Explorer
Preprocess Generate random data set Display & edit data Save data set into a file Open data set from different sources Filters for pre-processing Data summary Selected attribute summary Attribute display, selection & removal from the opened data set Visualise all attributes Selected attribute visualisation 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 Data Mining in Weka Explorer
Classify (as an example) Method List Selecting & Changing parameters Selecting a specific method

25 Data Mining in Weka Explorer
Visualisation Scatter plot of data object of different classes 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 = At confidence level of 95%, i.e. 1 -  = 0.95 and /2 = 0.025, n – 1 = 11, and therefore, t = The interval estimate is: The interval is estimated as [21.347, ] 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 Probability & Statistics: A Brief Review
Hypothesis testing (example) Assuming age = 25 Hypotheses: Null: Alternative: Calculating the statistic t as: Less than t = for p/2 = 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

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