Classifications of Dynamic Clustering based on Soft Computing

Data mining is major area in innovation of new trends in many application areas. The current applications generating data is are not static, it always changing day to day and it leads to changes in the technologies and data mining algorithms. Here, new study of data mining algorithms with dynamic characteristic. In various applications, data clustering is required for grouping of data based on similarity measures. For clustering on uncertain, incomplete and vague data soft computing approaches are efficient. In this paper, we considered dynamic characteristics of data and class structures into four cases and framed basic soft clustering methods for these cases. These methods are executed using fuzzy c means clustering in R programming language on suitable real time data set. We also focus on problems identified on each category.