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Data Mining and Knowledge Discovery Handbook, 2 Edition part 80

Data Mining and Knowledge Discovery Handbook, 2 Edition part 80. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 770 Mohamed Medhat Gaber Arkady Zaslavsky and Shonali Krishnaswamy Fig. 39.5. ANNCAD Framework tion sequence. FOCUS framework uses the difference between data mining models as the deviation in data sets. Ferrer-Troyano et al Ferrer-Troyano et al. 2004 have proposed a scalable classification algorithm for numerical data streams. The algorithm has been termed as Scalable Classification Algorithm by Learning decision Patterns SCALLOP. The algorithm starts by reading a number of user-specified labeled records. A number of rules are created for each class from these records. For each record read after creating these rules there are three cases a Positive covering a new record that strengthens a current discovered rule. b Possible expansion a new record that is associated with at least one rule however is not covered by any discovered rule. c Negative covering a new record that weakens a current discovered rule. For each of the above cases a different procedure is used as follows a Positive covering an update of the positive support and confidence of the rule is calculated and assigned to the existing rule. b Possible expansion the rule is extended if it satisfies two conditions 1. It is bounded within a user-specified growth bounds to avoid a possible wrong expansion of the rule. 2. There is no intersection between the expanded rule and any already discovered rule associated with the same class label. c Negative covering an update of the negative support and confidence is calculated. If the confidence is less than a minimum user-specified threshold a new rule is added. Having read a user-defined number of records a rule refining process takes place. Merge of rules in the same class and within a user-defined acceptable distance measure is used in this process with a condition non-intersecting with rules associated 39 Data Stream Mining 771 with other class labels. The resulting hypercube should also be within the growth bounds of the rules. The second step of the .