Chapter 4 Basic Data Mining Technique Chapter 1

Content • • What is classification? What is prediction? Supervised and Unsupervised Learning Decision trees Association rule K-nearest neighbor classifier • Genetic algorithm • Rough set approach • Fuzzy set approaches Case-based reasoning Data Warehouse and Data Mining 2 Chapter 4

Data Mining Process Data Warehouse and Data Mining 3 Chapter 4

Data Mining Strategies Data Warehouse and Data Mining 4 Chapter 4

Classification vs. Prediction • Classification: – predicts categorical class labels – classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and. . uses it in classifying new data • Prediction: – models continuous-valued functions, i. e. , predicts unknown or missing values Data Warehouse and Data Mining 5 Chapter 4

Classification vs. Prediction • Typical Applications – credit approval – target marketing – medical diagnosis – treatment effectiveness analysis Data Warehouse and Data Mining 6 Chapter 4

Classification Process 1. Model construction: 2. Model usage: Data Warehouse and Data Mining 7 Chapter 4

Classification Process 1. Model construction: describing a set of predetermined classes • Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute • The set of tuples used for model construction: training set • The model is represented as classification rules, decision trees, or mathematical formulae Data Warehouse and Data Mining 8 Chapter 4

1. Model Construction Classification Algorithms Training Data Classifier (Model) IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Data Warehouse and Data Mining 9 Chapter 4

Classification Process 2. Model usage: for classifying future or unknown objects Estimate accuracy of the model • The known label of test sample is compared with the classified result from the model • Accuracy rate is the percentage of test set samples that are correctly classified by the model • Test set is independent of training set Data Warehouse and Data Mining 10 Chapter 4

2. Use the Model in Prediction Classifier Testing Data Unseen Data (Jeff, Professor, 4) Tenured? Data Warehouse and Data Mining 11 Chapter 4

What Is Prediction? • Prediction is similar to classification – 1. Construct a model – 2. Use model to predict unknown value • Major method for prediction is regression – Linear and multiple regression – Non-linear regression • Prediction is different from classification – Classification refers to predict categorical class label – Prediction models continuous-valued functions Data Warehouse and Data Mining 12 Chapter 4

Issues regarding classification and prediction 1. Data Preparation 2. Evaluating Classification Methods Data Warehouse and Data Mining 13 Chapter 4

1. Data Preparation • Data cleaning – Preprocess data in order to reduce noise and handle missing values • Relevance analysis (feature selection) – Remove the irrelevant or redundant attributes • Data transformation – Generalize and/or normalize data Data Warehouse and Data Mining 14 Chapter 4

2. Evaluating Classification Methods • Predictive accuracy • Speed and scalability – time to construct the model – time to use the model • Robustness – handling noise and missing values • Scalability – efficiency in disk-resident databases • Interpretability: – understanding and insight proved by the model • Goodness of rules – decision tree size – compactness of classification rules Data Warehouse and Data Mining 15 Chapter 4

Supervised vs. Unsupervised Learning • Supervised learning (classification) – Supervision: The training data (observations, measurements, etc. ) are accompanied by labels indicating the class of the observations – New data is classified based on the training set • Unsupervised learning (clustering) – The class labels of training data is unknown – Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data Data Warehouse and Data Mining 16 Chapter 4

Supervised Learning Data Warehouse and Data Mining 17 Chapter 4

Unsupervised Learning Data Warehouse and Data Mining 18 Chapter 4

Classification by Decision Tree Induction • Decision tree – A flow-chart-like tree structure – Internal node denotes a test on an attribute – Branch represents an outcome of the test – Leaf nodes represent class labels or class distribution • Use of decision tree: Classifying an unknown sample – Test the attribute values of the sample against the decision tree Data Warehouse and Data Mining 19 Chapter 4

Classification by Decision Tree Induction • Decision tree generation consists of two phases 1. Tree construction • At start, all the training examples are at the root • Partition examples recursively based on selected attributes 2. Tree pruning • Identify and remove branches that reflect noise or outliers Data Warehouse and Data Mining 20 Chapter 4

Training Dataset This follows an example from Quinlan’s ID 3 Data Warehouse and Data Mining 21 Chapter 4

Output: A Decision Tree for “buys_computer” age? <=30 student? 30. . 40 overcast >40 credit rating? yes no yes excellent fair no yes Data Warehouse and Data Mining 22 Chapter 4

Decision Tree Data Warehouse and Data Mining 23 Chapter 4

What Is Association Mining? • Association rule mining: – • Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. Applications: – Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc. Data Warehouse and Data Mining 24 Chapter 4

Presentation of Classification Results Data Warehouse and Data Mining 25 Chapter 4

Instance-Based Methods • Instance-based learning: – Store training examples and delay the processing (“lazy evaluation”). . . until a new instance must be classified • Typical approaches – k-nearest neighbor approach • Instances represented as points in a Euclidean space. – Case-based reasoning • Uses symbolic representations and knowledge-based inference Data Warehouse and Data Mining 26 Chapter 4

The k-Nearest Neighbor Algorithm • All instances correspond to points in the n-D space. • The nearest neighbor are defined in terms of Euclidean distance. • The target function could be discrete- or real- valued. • For discrete-valued, the k-NN returns the most common value among the k training examples nearest to xq. • Vonoroi diagram: the decision surface induced by 1 -NN for a typical set of training examples. _ _ _ + _ . + xq Data Warehouse _ and Data Mining + . _ . + 27 . . . Chapter 4

Case-Based Reasoning • Also uses: lazy evaluation + analyze similar instances • Difference: Instances. . are not “points in a Euclidean space” • Methodology – Instances represented by rich symbolic descriptions (e. g. , function graphs) – Multiple retrieved cases may be combined Data Warehouse and Data Mining 28 Chapter 4

Genetic Algorithms • GA: based on an analogy to biological evolution • Each rule is represented by a string of bits • An initial population is created consisting of randomly generated rules – e. g. , IF A 1 and Not A 2 then C 2 can be encoded as 100 • Based on the notion of survival of the fittest, a new population is formed to consists of the fittest rules and their offsprings • The fitness of a rule is represented by its classification accuracy on a set of training examples • Offsprings are generated by crossover and mutation Data Warehouse and Data Mining 29 Chapter 4

Supervised genetic learning Data Warehouse and Data Mining 30 Chapter 4

Rough Set Approach • Rough sets are used to approximately or “roughly” define equivalent classes Data Warehouse and Data Mining 31 Chapter 4

Rough Set Approach • A rough set for a given class C is approximated by two sets: 1. a lower approximation (certain to be in C) and 2. an upper approximation (cannot be described as not belonging to C) • Finding the minimal subsets of attributes (for feature reduction) is NP-hard Data Warehouse and Data Mining 32 Chapter 4

Fuzzy Set Approaches • Fuzzy logic uses truth values between 0. 0 and 1. 0 to represent the degree of membership (such as using fuzzy membership graph) Fuzzy membeship Low Medium somewhat low High baseline high Income Data Warehouse and Data Mining 33 Chapter 4

Fuzzy Set Approaches • Attribute values are converted to fuzzy values – e. g. , income is mapped into the discrete categories {low, medium, high} with fuzzy values calculated • For a given new sample, more than one fuzzy value may apply • Each applicable rule contributes a vote for membership in the categories • Typically, the truth values for each predicted category are summed Data Warehouse and Data Mining 34 Chapter 4

Reference Data Mining: Concepts and Techniques (Chapter 7 for textbook) , Jiawei Han and Micheline Kamber, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada Data Warehouse and Data Mining 35 Chapter 4