Over the past decades, a prevalent amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Moreover a myriad of algorithms and methods has 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 effic...

The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a novel feedback framework for the cluster ensemble problem, which we call Wis...

Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netflix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and...

Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. We consider different heuristics to introduce diversity in cluster ensembles and study their individual and combined effect on the ensemble accuracy. Our experiments with three artificial and three real data sets, and 12 ensemble types, showed that the most successful...

This work reports the results of four ensemble approaches with the M5 model tree as the base regression model to anticipate Sodium Adsorption Ratio (SAR). Ensemble methods that combine the output of multiple regression models have been found to be more accurate than any of the individual models making up the ensemble. In this study additive boosting, bagging, rotation forest and random subspace...

Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. Such techniques typically involve the use of an individual feature significance evaluation, or a measurement of feature subset consistency, that work together with a search algorithm in order to determine a quality subset. Feature selection ensemble ai...

DNA microarray technologies make it possible to simultaneously monitor thousands of genes expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression level. Currently, various clustering methods have been proposed in the literature to classify cancer and nor...

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of high-accuracy base classifiers that should have high diversity in their pred...

The problem considered is cluster analysis with usage of the ensemble approach. The paper proposes a method for finding optimal weights for the averaged co-association matrix applied to the construction of the ensemble partition. The main idea is to find such weights for which the expectation of ensemble margin takes its maximum value. A latent variable pairwise classification model is used for...