A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
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With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
KeywordsMultimedia data bases Clustering Mining K means clustering Optimization
Funding was provided by General Program of the Natural Science Fund of Hubei Province (Grant No. 2014CFB916).
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