Research trends in data warehousing and data mining
- 1. Er. Nawaraj Bhandari Data Warehouse/Data Mining Classification and Prediction Chapter 8
- 2. Introduction There are two forms of data analysis that can be used for extracting models describing important classes or to predict future data trends. These two forms are as follows:  Classification  Prediction
- 3. Introduction  Classification models predict categorical class labels; and prediction models predict continuous valued functions.  For example, we can build a classification model to categorize bank loan applications as either safe or risky  Prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation.
- 4. What is classification?  Following are the examples of cases where the data analysis task is Classification:  A bank loan officer wants to analyze the data in order to know which customer (loan applicant) is risky or which are safe.  A marketing manager at a company needs to analyze a customer with a given profile, who will buy a new computer.  In both of the above examples, a model or classifier is constructed to predict the categorical labels. These labels are risky or safe for loan application data and yes or no for marketing data.
- 5. What is prediction?  Following are the examples of cases where the data analysis task is Prediction:  Suppose the marketing manager needs to predict how much a given customer will spend during a sale at his company.  In this example we are bothered to predict a numeric value. Therefore the data analysis task is an example of numeric prediction. In this case, a model or a predictor will be constructed that predicts a continuous-valued-function or ordered value.  Regression analysis is a statistical methodology that is most often used for numeric prediction.
- 6. How Does Classification Works? With the help of the bank loan application that we have discussed above, let us understand the working of classification. The Data Classification process includes two steps:  Building the Classifier or Model  Using Classifier for Classification
- 7. Building the Classifier or Model  This step is the learning step or the learning phase.  In this step the classification algorithms build the classifier.  The classifier is built from the training set made up of database tuples and their associated class labels.  Each tuple that constitutes the training set is referred to as a category or class. These tuples can also be referred to as sample, object or data points.
- 8. Building the Classifier or Model
- 9. Using Classifier for Classification  In this step, the classifier is used for classification.  Here the test data is used to estimate the accuracy of classification rules.  The classification rules can be applied to the new data tuples if the accuracy is considered acceptable.
- 10. Classification by Decision Tree Induction Decision tree induction is the learning of decision trees from class labeled training tuples. Decision tree is a flowchart-like tree structure where internal nodes (non leaf node) denotes a test on an attribute branches represent outcomes of tests Leaf nodes (terminal nodes) hold class labels and Root node is the topmost node.
- 11. Classification by Decision Tree Induction
- 12. Classification by Decision Tree Induction Example RID age income student credit-rating Class 1 youth high no fair ? Test on age: youth Test of student: no Reach leaf node Class NO: the customer Is Unlikely to buy a computer
- 13. Algorithm for constructing Decision Tress Constructing a Decision tree uses greedy algorithm. Tree is constructed in a top-down recursive divide- and-conquer manner. • At start, all the training tuples are at the root • Tuples are partitioned recursively based on selected attributes • If all samples for a given node belong to the same class Label the class • If There are no remaining attributes for further partitioning Majority voting is employed for classifying the leaf • There are no samples left Label the class and terminate • Else Got to step 2
- 14. K-Mean Algorithms 1. Take mean value 2. Find nearest number of mean and put in cluster. 3. Repeat one and two until we get same mean
- 15. References 1. Sam Anahory, Dennis Murray, “Data warehousing In the Real World”, Pearson Education. 2. Kimball, R. “The Data Warehouse Toolkit”, Wiley, 1996. 3. Teorey, T. J., “Database Modeling and Design: The Entity-Relationship Approach”, Morgan Kaufmann Publishers, Inc., 1990. 4. “An Overview of Data Warehousing and OLAP Technology”, S. Chaudhuri, Microsoft Research 5. “Data Warehousing with Oracle”, M. A. Shahzad 6. “Data Mining Concepts and Techniques”, Morgan Kaufmann J. Han, M Kamber Second Edition ISBN : 978-1-55860-901-3
- 16. ANY QUESTIONS?