The majority voting and accurate prediction of classification algorithm in data mining are challenging task for data classification. For the improvement of data classification used different classifier along with another classifier in a manner of ensemble process. Ensemble process increase the classification ratio of classification algorithm, now such par diagram of classification algorithm is ...

This paper discusses one method of clustering a high dimensional dataset using dimensionality reduction and context dependency measures (CDM). First, the dataset is partitioned into a predefined number of clusters using CDM. Then, context dependency measures are combined with several dimensionality reduction techniques and for each choice the data set is clustered again. The results are combine...

Cluster ensemble algorithms have been used in different field like data mining, bioinformatics and pattern recognition. Many of them use label correspondence as a step which can be performed with some accuracy if all the input partitions are generated with same k. Thus these algorithms produce good results if this k is close to the actual number of clusters in the dataset. This puts great restr...

We study the use of red sequence selected galaxy spectroscopy for unbiased estimation of galaxy cluster masses. We use the publicly available galaxy catalog produced using the semi-analytic model of De Lucia & Blaizot (2007) on the Millenium Simulation (Springel et al. 2005). We make mock observations to mimic the selection of the galaxy sample, the interloper rejection and the dispersion measu...

To improve the performance of clustering ensemble method, a selective fuzzy clustering ensemble algorithm is proposed. It mainly includes selection of clustering ensemble members and combination of clustering results. In the process of member selection, measure method is defined to select the better clustering members. Then some selected clustering members are viewed as hyper-graph in order to ...

Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gløshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best n...