Consensus clustering emerges as a promising solution to find cluster structures from data. As an efficient approach for consensus clustering, the Kmeans based method has garnered attention in the literature, but the existing research is still preliminary and fragmented. In this paper, we provide a systematic study on the framework of K-meansbased Consensus Clustering (KCC). We first formulate t...

This paper presents an evolutionary algorithm that uses the combination of the selection operator “the best” and the proposed operators, crossover “crossover-k” and intelligent mutation “mutation-S.” The initial population (feasible individuals) is generated with the kmeans algorithm of clustering (Data-Mining Techniques). The proposed algorithm solves the Vehicle Routing Problem with Time Wind...

Moving object indexing is an important technique in location based services. This paper proposes the velocity partitioning technique which exploits the important property of skewed velocity distributions. The VP technique firstly finds the dominant velocity axes (DVAs) using principal components analysis (PCA) and kmeans clustering. Then the moving objects are indexed using the DVAs and removin...

For the peak picking of tempo candidates, applying kmeans clustering on tempo curve is straightforward and leading to good result. But the tempo candidates obtained from tempo curve are limited and lose a lot of information for possible tempi. The study proposes the local maximum peak picking method to increase the number and information of possible tempo candidates. Therefore, the accuracy of ...

Central and subspace clustering methods are at the core of many segmentation problems in computer vision. However, both methods fail to give the correct segmentation in many practical scenarios, e.g., when data are close to the intersection of subspaces or when two cluster centers in different subspaces are spatially close. In this paper, we address these challenges by considering the problem o...

Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Interpretability and reliability of clustering results is vital, as decisions based on them affec...

Cartoons are one type of illustration usually in a non-realistic or semi-realistic style. To make cartoon drawing manually requires good ability. So, not everyone can cartoons. This research proposes non-photorealistic rendering algorithm to create drawings automatically. The consists four phases. First, an image abstraction using bilateral filtering. Second, kmeans clustering for abstract quan...

Data mining or mining customer’s data helps to discover the key characteristics from the customer’s data, and possibly use those characteristics for future prediction. The problem of selecting the “best” algorithm/parameter setting is a difficult one. However kMeans Clustering is an algorithm helps to classify or to group the objects based on attributes/features into k number of groups. A good ...

Received Apr 18, 2017 Revised May 30, 2017 Accepted Jun 15, 2017 A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the te...

Central and subspace clustering methods are at the core of many segmentation problems in computer vision. However, both methods fail to give the correct segmentation in many practical scenarios, e.g., when data are close to the intersection of subspaces or when two cluster centers in different subspaces are spatially close. In this paper, we address these challenges by considering the problem o...