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The aim of this study was to solve the problems of mixed multiattribute group decision, which have the interval-valued, linguistic and interval-valued intuitionistic fuzzy number. As a solution to this problem, we propose a decision-making method that the decision maker weight was unknown, however, the attribute weight was known as well as the score function with transformation parameters. Firstly, conversion formula was used to make the mixed evaluation information unified transformed to the interval-valued intuitionistic fuzzy number. Furthermore, the decision maker and attribute weight were determined by the interval-valued intuitionistic fuzzy numbers distance and expectation values. Under the instruction of intuitionistic fuzzy weighted averaging operator, decision maker weight and attribute weight, we aggregated the individual decision making matrix and scheme different attribute successively. Additionally, we also made the dynamic scheduling analysis by the score function with transformation parameter. Finally, this method feasibility was testified by the example solving.

Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Clustering medical images for intelligent decision support is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly quantify the domain knowledge about brain image, and then incorporate this quantified measurement into the clustering algorithm. Our algorithm contains two parts: (1) clustering regions of interest (ROI) detected from brain image; (2) clustering images based on the similarity of ROI. We apply the method to cluster brain images and present results to demonstrate its usefulness and effectiveness.

The high incidence of brain disease, especially brain tumor, has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors' diagnosis. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. These patterns are significant in medical images because it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are satisfying.

Clustering medical images is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly quantify the domain knowledge about brain image, and then incorporate this quantified measurement into the clustering algorithm. Our algorithm contains two parts: (1) clustering regions of interest (ROI) detected from brain image; (2) clustering images based on the similarity of ROI. We apply the method to cluster brain images and present results to demonstrate its usefulness and effectiveness.