REVIEW ON DATA MINING TECHNIQUES FOR SUBGROUP DISCOVERY

Subgroup discovery is a data mining technique which focuses fascinating rules regarding a target variable. A paramount feature for this method is the combination of predictive and descriptive induction. This survey gives highlights on the establishments, algorithms, and progressed studies together with the applications of subgroup discovery. This paper shows a novel data mining systems for the investigation and extraction of learning from information created by electricity meters. In spite of the fact that a rich source of data for energy utilization analysis, power meters deliver a voluminous, quick paced, transient stream of information those traditional methodologies are not able to address altogether. So as to beat these issues, it is imperative for a data mining framework to consolidate usefulness for break summarization and incremental analysis utilizing intelligent procedures. In subgroups whose sizes are large and patterns are not usual has to be discovered. Their models have to be generated first. The many algorithms have been used to overcome the wider range of data mining problems. This paper gives a survey on subgroup discovery patterns from smart electricity meter data