  With the advancement in image capturing device, the image data been generated
at high volume. If images are analyzed properly, they can reveal useful
information to the human users. Content based image retrieval address the
problem of retrieving images relevant to the user needs from image databases on
the basis of low-level visual features that can be derived from the images.
Grouping images into meaningful categories to reveal useful information is a
challenging and important problem. Clustering is a data mining technique to
group a set of unsupervised data based on the conceptual clustering principal:
maximizing the intraclass similarity and minimizing the interclass similarity.
Proposed framework focuses on color as feature. Color Moment and Block
Truncation Coding (BTC) are used to extract features for image dataset.
Experimental study using K-Means clustering algorithm is conducted to group the
image dataset into various clusters.
