one of the most important aspects of software project management is the estimation of cost and time required for running information system. therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. software cost estimation refers to the process of development requirement prediction of software system. various kinds of effort estimation patter...

Exising method for supervised clustering called Evolutionary Distance Metric Learning (EDML) has never been compared to other clustering method. This work conducted experiments to compare EDML with other semisupervised clusterings, such as COP-Kmeans and other DML methods. The result empirically confirms that EDML gives better clustering structure than the candidate clustering methods-i.e. K-me...

Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In ...

We consider the problem of efficiently executing data clustering queries in a client-server setting. Specifically, we consider an environment in which the entire data set is housed on a server and a client is interested in interactively performing kMeans clustering on different subsets of this data set. Extant solutions to this problem suffer from (a) a significant amount of remote I/O and (b) ...

While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance b...

In this paper we present a comparison of multiple cluster algorithms and their suitability for clustering text data. The clustering is based on similarities only, employing the Kolmogorov complexity as a similiarity measure. This motivates the set of considered clustering algorithms which take into account the similarity between objects exclusively. Compared cluster algorithms are Median kMeans...

Clustering is the process of grouping of data, where the grouping is established by finding similarities between data based on their characteristics. Such groups are termed as Clusters. A comparative study of clustering algorithms across two different data items is performed here. The performance of the various clustering algorithms is compared based on the time taken to form the estimated clus...