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A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures

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Abstract

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.

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Acknowledgements

Funding was provided by General Program of the Natural Science Fund of Hubei Province (Grant No. 2014CFB916).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan, China

    Xiaoping Jiang, Chenghua Li & Jing Sun

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  1. Xiaoping Jiang
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  2. Chenghua Li
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  3. Jing Sun
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Correspondence to Chenghua Li.

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Jiang, X., Li, C. & Sun, J. A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures. Cluster Comput 21, 797–804 (2018). https://doi.org/10.1007/s10586-017-0949-6

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  • DOI: https://doi.org/10.1007/s10586-017-0949-6

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