Though many cluster ensemble approaches came forward as a potential and dominant method for enhancing the robustness, stability and the quality of individual clustering systems, it is intensely observed that this approach in most cases generate a final data partition with deficient information. The primary ensemble information matrix generated in the traditional cluster ensemble approaches resu...

Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient ...

the objective of this study was to classify the shoosh aquifer to several zones with different water quality in khuzestan province, iran. in this regard, the performance of classification methods (discriminant function and cluster analysis) for the classification of groundwater based on the level of pollution with an emphasis on the problem of over-fitting in training data were considered. an o...

We study, in the ensemble of constant particle number, processes in which a cluster of particles is annihilated and particles are created catalytically in active sites. In this ensemble, particles belonging to a cluster of l particles jump to l distinct active sites. As examples of our prescription, we analyze numerically three nonequilibrium systems that annihilate cluster of particles that ar...

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases:...

Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength...

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integrati...

Cluster algebras are a remarkable discovery of S. Fomin and A. Zelevinsky [FZI]. Cluster algebras are certain commutative algebras defined by a very simple and general data. Below we consider only cluster algebras of geometric origin, which are quite general and probably the most important examples of cluster algebras. We show that a cluster algebra of geometric origin is part of a richer struc...

Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...