A distributed k-mean clustering algorithm for cloud data mining

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-30 Number-7
Year of Publication : 2015
Authors : Renu Asnani
  10.14445/22315381/IJETT-V30P263

MLA 

Renu Asnani"A distributed k-mean clustering algorithm for cloud data mining", International Journal of Engineering Trends and Technology (IJETT), V30(7),341-345 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
cloud computing is a new generation computational manner. In this technique the way of computing is transformed into distributed computing, therefore the concept of cloud is used where the efficient computational experience and scalable computing is required. Not only has the cloud provided the scalable computing it also provides the scalable storage units. Therefore a significant amount of data is arrived in these units to handle them, among various kinds of storage the unstructured data storage is also a part of entire cloud data storage i.e. social networking text data, images and other electronic form of documents. Thus in this presented work a survey is introduced for cloud data storage, and their cluster analysis for utilizing the data into various business intelligence applications. in addition of that a new model of cluster analysis of data is proposed which provides the clustering as service.

 References

[1] Data mining Concepts and Techniques, Second Edition, Jiawei Han and Micheline Kamber, http://akademik.maltepe.edu.tr/~kadirerdem/772s_Data.Mini ng.Concepts. and .Techniques.2nd.Ed.pdf.
[2] “Data Mining - Classification & Prediction Introduction”, http://www.idc-online.com/ technical_references/pdfs/data_communications/Data_Minin g_Classification_Prediction.pdf
[3] Data Mining - Cluster Analysis, http://www.tutorialspoint.com/data_mining/dm_cluster_anal ysis.htm
[4] Pavel Berkhin, “Survey of Clustering Data Mining Techniques”, Accrue Software, 1045 Forest Knoll Dr., San Jose, CA, 95129
[5] Marina Meil˘a, “The stability of a good clustering”, Journal of Artificial Intelligence Research 1 (1993) 1-15 Submitted 6/91; published 9/91.
[6] Changqing Ji, Yu Li, Wenming Qiu, Uchechukwu Awada, Keqiu Li, “Big Data Processing in Cloud Computing Environments”, 2012 International Symposium on Pervasive Systems, Algorithms and Networks
[7] Yi Zhuang, Nan Jiang, Zhiang Wu, Qing Li, Dickson K.W. Chiu, Hua Hu, “Efficient and robust large medical image retrieval in mobile cloud computing environment”, 2013 Elsevier Inc. All rights reserved.
[8] Astha Pareek, Manish Gupta, “Review of Data Mining Techniques in Cloud Computing Database”, International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970), Volume-2 Number-2 Issue-4 June-2012
[9] Ibrahim Abaker Targio Hashem, Ibrar Yaqoob, Nor Badrul Anuar, Salimah Mokhtar, Abdullah Gani, Samee Ullah Khan, “The rise of “big data” on cloud computing: Review and open research issues”, & 2014 Elsevier Ltd. All rights reserved.
[10] Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein, “Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud”, Proceedings of the VLDB Endowment, Vol. 5, No. 8 Copyright 2012.

Keywords
cloud computing, cluster analysis, data mining, social networking, text clustering.