REVIEW ON DATA MINING TECHNIQUES FOR SUBGROUP DISCOVERY
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 4, Issue 6
Abstract
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 infor mation 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 a ltogether. 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 h as 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.
Authors and Affiliations
Deepali Nidhan Gunjate
Keywords
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