In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute traditional distance or similarity measures with new metrics existing centralized algorithms order tackle uncertainty data. However, perform clustering, representation plays an i...

This paper introduces clustering as a tool to improve the eeects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is rst clustered and the solutions in each of the clusters are processed separately. This alleviates the problem of symmetry which is often a major diiculty of many evolutionary algorithms in combi...

In this paper we present algorithms for story segmentation and topic detection. Both algorithms are online algorithms and use a combination of machine learning, statistical natural language processing and information retrieval techniques. The story segmentation algorithm is a two stage algorithm that uses a decision tree based probabilistic model in the rst stage and incorporates aspects of our...

In this paper we present algorithms for story segmentation and topic detection. Both algorithms are online algorithms and use a combination of machine learning, statistical natural language processing and information retrieval techniques. The story segmentation algorithm is a two stage algorithm that uses a decision tree based probabilistic model in the rst stage and incorporates aspects of our...

This paper proposes a new approach for discriminative clustering. The intuition is, for a good clustering, one should be able to learn a classifier from the clustering labels with high generalization accuracy. Thus we define a novel metric to evaluate the quality of a clustering labeling, named Minimum Separation Probability (MSP), which is a lower bound of the generalization accuracy of a clas...

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn th...

In a strapdown magnetic compass, heading angle is estimated using the Earth's magnetic field measured by Three-Axis Magnetometers (TAM). However, due to several inevitable errors in the magnetic system, such as sensitivity errors, non-orthogonal and misalignment errors, hard iron and soft iron errors, measurement noises and local magnetic fields, there are large error between the magnetometers'...

<p><span>Classification of information is a vague and difficult to explore area research, hence the emergence grouping techniques, often referred Clustering. It necessary differentiate between an unsupervised supervised classification. Clustering methods are numerous. Data partitioning hierarchization push use them in parametric form or not. Also, their influenced by algorithms prob...