fast and high-quality Intrusion Detection algorithms play an important role in providing security management component by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful groups of data via network log file are ideal tools for their interactive visualization and exploration as they provide a powerful m...

A new online clustering method called E2GK (Evidential Evolving Gustafson-Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson-Kessel (EGK) algorithm. Online partitionin...

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Fl...

Key frames and previews are two forms of a video abstract, widely used for various applications in video browsing and retrieval systems. We propose in this paper a novel method for generating these two abstract forms for an arbitrary video sequence. The underlying principle of the proposed method is the removal of the visual-content redundancy among video frames. This is done by first applying ...

The paper presents a clustering method which can be applied to populated ontologies for discovering interesting groupings of resources therein. The method exploits a simple, yet effective and languageindependent, semi-distance measure for individuals, that is based on their underlying semantics along with a number of dimensions corresponding to a set of concept descriptions (discriminating feat...

The paper discusses yet another approach of clustering datasets whose cluster numbers are not known beforehand. The suggested approach effectively determines the number of clusters or partitions while running the algorithm. The proposed method is only limited to partitional clustering inspired from the K-means algorithm. In this work a Modified TeachingLearning-Based Optimization (MTLBO) is use...

A new spectral-spatial classification scheme for hyperspectral images is presented. Pixel-wise Support Vector Machines classification and segmentation are performed independently, and then the results are combined, using the majority vote approach. Thus, every region from a segmentation map defines an adaptive neighborhood for all the pixels within this region. The use of several segmentation t...

This paper presents a personalized approach for distributed trust management by employing the k-means range algorithm, a combination of the partitional kmeans clustering algorithm with orthogonal range search concepts. The aim of this approach is to aid the human or computer agent in organizing information from multiple sources into clusters according to its “trust features”. Thus the agent can...

This paper combines three exploratory data analysis methods, principal component methods, hierarchical clustering and partitioning, to enrich the description of the data. Principal component methods are used as preprocessing step for the clustering in order to denoise the data, transform categorical data in continuous ones or balanced groups of variables. The principal component representation ...

Data mining techniques help in business decision making and predicting behaviors and future trends. Clustering is a data mining technique used to make groups of objects that are somehow similar in characteristics. Clustering analyzes data objects without consulting a known class label or category i.e. it is an unsupervised data mining technique. Kmeans is a widely used partitional clustering al...