Clustering and Query Optimization in Fuzzy Object-Oriented Database

Abstract

The purpose of the clustering method is to provide some meaningful partitioning of the data set. In general, finding separate clusters with similar members is essential. A problem in clustering is how to determine the number of optimal clusters that best fits the data set. Most clustering algorithms generate a partition based on input parameters (for example, cluster number, minimum density) which results in limiting the number of clusters. Therefore, the article proposes an improved EMC clustering algorithm that is more flexible in handling and manipulating those clusters, where input parameter values are assumed to be different clusters for different partitions of a data set. In addition, based on the above partitioning results, this article proposes a new approach to processing and optimizing fuzzy queries to improve efficiency in the manipulation and processing of specific data such as (less time consuming, less resource consuming)