Suggested techniques for clustering and mining of data streams

Author

Anuradha, G. ; Roy, Bidisha

Author_Institution

Dept. of Comput. Eng., St. Francis Inst. of Technol., Mumbai, India

fYear

2014

fDate

4-5 April 2014

Firstpage

265

Lastpage

270

Abstract

The buzz word in research is Big Data. Big Data gets characterized by 5 V´s: Volume, Velocity, Variety, Veracity and Value of data. Volume in order of penta bytes, velocity which refers to click stream data in various domains, variety comprising of heterogeneous data, veracity indicating the cleanliness of data and value emphasizing on the return on investment for companies who invest in Big Data technologies. This Big Data is better modeled not as persistent tables but in the form of transient data streams which need different clustering and mining techniques to be effectively processed and managed. In this paper some suggestions on online learning through clustering and mining of stream data are presented.

Keywords

Big Data; data mining; pattern clustering; Big Data technology; buzz word; data stream clustering technique; data stream mining technique; data value; data variety; data velocity; data veracity; data volume; heterogeneous data; online learning; return on investment; transient data streams; Algorithm design and analysis; Big data; Classification algorithms; Clustering algorithms; Data mining; Heuristic algorithms; Prediction algorithms; Big Data; Clustering; Data Streams; Mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits, Systems, Communication and Information Technology Applications (CSCITA), 2014 International Conference on

Conference_Location

Mumbai

Type

conf

DOI

10.1109/CSCITA.2014.6839270

Filename

6839270