Extracting defined information from the huge datasets are challenging task for many researchers, especially input dataset like images are too complex, because image data consists of motion, time, text, audio and pixel difference. From this huge complex dataset, extracting the domain knowledge will take more time. This image extraction differs from traditional text mining, due to the nature of image datasets. Extracting information from image data requires domain knowledge and users have to concentrate more on the domain. Advancement of technology allows the user to create more and more image datasets with no guarantee of quality. This paper focuses on image mining performance with help of a hierarchical clustering technique. In the proposed techniques, video data are grouped into frames, then duplicate reaming frames are eliminated and stored in the database for further operations. Entire works is divided into client and server side operations. The proposed technique works well and the experimental results are also verified.