A Review on Various Approaches for data Preserving Clustering in Data Mining
Keywords:
Clustering, K-Means, K-medioid, CobWeb, and DBSCANAbstract
Data Mining is the process of extraction of valuable information from the raw data. Classification and clustering are the two main components that are used in data mining process.In the process of data mining various approaches have been used for clustering process so that data can be managed in the form of clusters. In this paper various approaches of clustering has been discussed. Various approaches have been used for clustering based on properties of the dataset instances. These approaches are based on centroid, rule based clustering and properties based clustering. On the basis of these approaches clustering approach that is suitable for large dataset has been selected.
References
- Mr. G. Nirmal Kumar. "A system learning of connection with humans by online social networking - A rapport by means of creating usable customer Intelligence from Social media Data" IEEE International Conference on Science, Engineering and Management Research, 2014,pp. 1 - 6.
- EssamShaaban."A Proposed Churn Prediction Model"International Journal,Research and Applications 2012, pp.693-697.
- Xi Long."Churn Analysis of Online Social Network Users Using Data Mining Techniques"international Multi-conference of engineers and computer scientists,2012,pp-14-16.
- D. S. RAJPUT."Analysis of Social Networking Sites Using K- Mean Clustering Algorithm" International Journal of Computer & Communication Technology, 2012, Vol. 3, pp. 975 -979.
- Chamatkar, A.J."Implementation of Different Data Mining Algorithms with Neural Network"IEEE International Conference onComputing Communication Control and Automation,2015, pp. 374-378.
- Huan Liu. "Some computational challenges in mining social media" IEEE International Conference on Advances in Social Networks Analysis and Mining, 2013, pp. 25-28
- Au, W. H."A novel evolutionary data mining algorithm with applications to churn prediction" IEEE International Conference on Evolutionary Computation,2003,pp. 532 - 545.
- Gok, Mehmet."A case study for the churn prediction in Turksat internet service subscription" IEEE International Conference on Advances in Social Networks Analysis and Mining,2015,pp. 1220-1224.
- GuangliNie."Find Intelligent Knowledge by Second-Order Mining: Three Cases from China" IEEE International Conference on Data Mining Workshops,2010,pp. 1189-1195.
- Chen Yu-bao."Study on Predictive Model of Customer Churn of Mobile Telecommunication Company" IEEE International Conference on Business Intelligence and Financial Engineering,2011,pp. 114 - 117.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST
This work is licensed under a Creative Commons Attribution 4.0 International License.
