An Attribute Weighted Fuzzy Clustering Algorithm for Mixed Crime Data

Background/Objectives: The main objective is to design and develop a clustering algorithm for finding similar sub sets from crime data. This paper focuses a method for developing an algorithm and modify the existing technique in three ways, such as i) new attribute weightage scheme instead of IGR, ii) suitability to mixed data and iii) using FCM-based clustering instead of k-means. Methods/Statistical analysis: Generally, the effectiveness of clustering algorithm is completely based on distance matching that finds the similarity between data records and centroid. Giving equal importance for all the attributes is not much effective in clustering process. Instead, attribute weightage could be included in distance matching. A weight vector is generated based on mutual information. The method for attribute weightage is common for both numerical and categorical data. Finally, the grouping of similar sub sets is done based on FCM-based clustering procedure in which the distance matching is carried out based on the attribute weights. Findings: The experimental analysis has done using crime and hepatitis datasets where the performance of the proposed clustering algorithm has been analyzed. Results show that proposed FCM method has good accuracy than the AK-mode. Application/Improvements: Proposed method plays an important role in crime domain for better prediction. Type II fuzzy can also be used for better closeness analysis.