AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM
Journal: ICTACT Journal on Soft Computing (IJSC) (Vol.6, No. 2)Publication Date: 2016-01-01
Authors : Saravanan Suba; T. Christopher;
Page : 1171-1176
Keywords : Apriori; FP-Tree; TR-FCTM; Minimum Support;
Abstract
Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method) discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.
Other Latest Articles
- RECOGNITION OF TAMIL SYLLABLES USING VOWEL ONSET POINTS WITH PRODUCTION, PERCEPTION BASED FEATURES
- ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION
- R-GA: AN EFFICIENT METHOD FOR PREDICTIVE MODELING OF MEDICAL DATA USING A COMBINED APPROACH OF RANDOM FORESTS AND GENETIC ALGORITHM
- SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK
- AN EFFECTIVE MULTI-CLUSTERING ANONYMIZATION APPROACH USING DISCRETE COMPONENT TASK FOR NON-BINARY HIGH DIMENSIONAL DATA SPACES
Last modified: 2016-09-15 14:58:02