Subgroup Discovery a Data Mining Technique: Immense Survey

Deepali Nidhan Gunjate, B. R. Kanawade

Abstract: Subgroup discovery is a data mining procedure which concentrates fascinating rules regarding a target variable. A paramount feature for this task is the mixture of predictive and descriptive induction. An overview identified with the assignment of subgroup discovery is presented here. This survey concentrates 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.

Keywords: Subgroup discovery, data mining, pattern recognition, data analysis, knowledge discovery

How to Cite?: Deepali Nidhan Gunjate, B. R. Kanawade, "Subgroup Discovery a Data Mining Technique: Immense Survey", Volume 4 Issue 6, June 2015, International Journal of Science and Research (IJSR), Pages: 1102-1105, https://www.ijsr.net/getabstract.php?paperid=SUB155308, DOI: https://dx.doi.org/10.21275/SUB155308