Abstract: In the domain of Image processing, Image mining is advancement in the field of data mining. Image mining is the extraction of hidden data, association of image data and additional pattern which are quite not clearly visible in image. It’s an interrelated field that involves, Image Processing, Data Mining, Machine Learning, Artificial Intelligence and Database. The lucrative point of Image Mining is that without any prior information of the patterns it can generate all the significant patterns. This is the writing for a research done on the assorted image mining and data mining techniques. Data mining refers to the extracting of knowledge /information from a huge database which is stored in further multiple heterogeneous databases. Knowledge/ information is communicating of message through direct or indirect technique. These techniques include neural network, clustering, correlation and association. This writing gives an introductory review on the application fields of data mining which is varied into telecommunication, manufacturing, fraud detection, and marketing and education sector. In this technique we use size, texture and dominant colour factors of an image. Gray Level Co-occurrence Matrix (GLCM) feature is used to determine the texture of an image. Features such as texture and color are normalized. The image retrieval feature will be very sharp using the texture and color feature of image attached with the shape feature. For similar types of image shape and texture feature, weighted Euclidean distance of color feature is utilized for retrieving features.

Keywords: Data Mining, Image Mining, Feature Extraction, Image Retrieval, Association, Clustering, knowledge discovery database,Gray Level Co-occurrence Matrix, centroid, Weighted Euclidean Distance.