Dominant sets are a new graph-theoretic concept that has proven to be relevant in partitional (flat) clustering as well as image segmentation problems. However, in many computer vision applications, such as the organization of an image database, it is important to provide the data to be clustered with a hierarchical organization, and it is not clear how to do this within the dominant set framew...

Clustering divides data objects into groups to minimize the variation within each group. This technique is widely used in data mining and other areas of computer science. K-means is a partitional clustering algorithm that produces a fixed number of clusters through an iterative process. The relative simplicity and obvious data parallelism of the K-means algorithm make it an excellent candidate ...

S.Keerthana1, Mrs. S. Akila2 1Research Scholar, Department of Computer Science, Vellalar College for Women, Erode, Tamilnadu, India 2Assistant Professor, Dept. of Computer Science, Vellalar College for Women, Erode, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Clustering is a...

This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocation algorithms involving the construction of the clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by l...

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficie...

K-means is undoubtedly themostwidely used partitional clustering algorithm. Unfortunately, due to the nonconvexity of the model formulations, expectation-maximization (EM) type algorithms converge to different local optima with different initializations. Recent discoveries have identified that the global solution of K-means cluster centroids lies in the principal component analysis (PCA) subspa...

Player classification has recently become a key aspect of game design in areas such as adaptive game systems, player behaviour prediction, player tutoring and non-player character design. Past research has focused on the design of hierarchical, preferencebased and probabilistic models aimed at modelling players' behaviour. We propose a meta-classification approach that breaks the clustering of ...

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, this algorithm is highly sensitive to the initial selection of the cluster centers. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have superlinear complexity in the number of data points, which makes them impractical for large data sets. On ...

Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thu...

In recent years, co-clustering has emerged as a powerful data mining tool that can analyze dyadic data connecting two entities. However, almost all existing co-clustering techniques are partitional, and allow individual rows and columns of a data matrix to belong to only one cluster. Several current applications, such as recommendation systems and market basket analysis, can substantially benef...