Clustering algorithms in general need the number of clusters as a priori, which is mostly hard for domain experts to estimate. In this paper, we use Niched Pareto k-means Genetic Algorithm (GA) for clustering. After running the multi-objective GA, we get the pareto-optimal front that gives the optimal number of clusters as a solution set. We analyze the clustering results using several cluster ...

Trajectory clustering has been used to very effectively in the detection of anomalous behavior in video sequences. A key point in trajectory clustering is how to measure the (dis)similarity between two trajectories. This paper deals with a new dissimilarity measure for trajectory clustering, giving the same importance to differences and similarities between the trajectories. Experimental result...

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clust...

Clustering techniques are widely used in “Web Usage Mining” to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are...

An approach to determining the optimal cell number of manufacturing cell formation is presented. Firstly, the difference of weighting exponent, cluster center and metrics how to have an impact upon the clustering results and membership function are studied. Secondly, a method to determine the optimal m value is given. Two-order partial derivative of the objective function for FCM is calculated,...

Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. Traditional clustering algorithms usually predefine the number of clusters via random selection or contend based knowledge. An improper pre-selection for the number of clusters may easily lead to bad clustering outcome. In order to address this issue we propose in this paper a new ev...

The quality of results for partitioning clustering algorithms depends on the assumption made on the number of clusters presented in the data set. Applying clustering methods on real data missing values turn out to be an additional challenging problem for clustering algorithms. Fuzzy clustering approaches adapted to incomplete data perform well for a given number of clusters. In this study, we a...

Sequence clustering is one of most fundamental topics which can be applied in various research field. Most of previous work on sequence clustering is dedicated to the single-label clustering in which the whole similarity of equal-length sequence is considered and measured by Euclidean distance function. However, intrinsic properties behind sequence demand the multi-label clustering. In addition...

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different ...