Title page for ETD etd-01242005-144012


Type of Document Master's Thesis
Author Nayar, Arun B
URN etd-01242005-144012
Title A Study of Distributed Clustering of Vector Time Series on the Grid by Task Farming
Degree Master of Science in Industrial Engineering (M.S.I.E.)
Department Industrial & Manufacturing Systems Engineering
Advisory Committee
Advisor Name Title
Warren Liao Committee Chair
Gabrielle Allen Committee Member
Jianhua Chen Committee Member
Keywords
  • task farming
  • master-worker
  • grid computing
  • data mining
  • clustering
  • distributed
Date of Defense 2004-12-14
Availability unrestricted
Abstract
Traditional data mining methods were limited by availability of computing resources like network bandwidth, storage space and processing power. These algorithms were developed to work around this problem by looking at a small cross-section of the whole data available. However since a major chunk of the data is kept out, the predictions were generally inaccurate and missed out on significant features that was part of the data. Today with resources growing at almost the same pace as data, it is possible to rethink mining algorithms to work on distributed resources and essentially distributed data. Distributed data mining thus holds great promise. Using grid technologies, data mining can be extended to areas which were not previously looked at because of the volume of data being generated, like climate modeling, web usage, etc. An important characteristic of data today is that it is highly decentralized and mostly redundant. Data mining algorithms which can make efficient use of distributed data has to be thought of. Though it is possible to bring all the data together and run traditional algorithms, this has a high overhead, in terms of bandwidth usage for transmission, preprocessing steps which have to be to handle every format the received data. By processing the data locally, the preprocessing stage can be made less bulky and also the traditional data mining techniques would be able to work on the data efficiently. The focus of this project is to use an existing data mining technique, fuzzy c-means clustering to work on distributed data in a simulated grid environment and to review the performance of this approach viz., the traditional approach.
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