We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which ca...

This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering us...

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights...

Clustering is a core problem in data-mining with innumerable applications spanning many fields. A key difficulty of effective clustering is that for unlabelled data a ‘good’ solution is a somewhat ill-defined concept, and hence a plethora of valid measures of cluster quality have been devised. Most clustering algorithms optimize just one such objective (often implicitly) and are thus limited in...

We present a method to reduce a formal context while retaining much its information content. Although simple, our ICRA approach offers an effective way to reduce the complexity of concept lattices and / or knowledge spaces by changing only little information in comparison to a competing model which uses fuzzy K-Means clustering.

The problems of finding alternative clusterings and avoiding bias have gained popularity over the last years. In this paper we put the focus on the quality of these alternative clusterings, proposing two approaches based in the use of negative constraints in conjunction with spectral clustering techniques. The first approach tries to introduce these constraints in the core of the constrained no...

We propose a method for creating different types of study groups with aim to support effective collaboration during learning. We concentrate on the small groups which solve short-term well-defined problems. The method is able to apply many types of students’ characteristics as inputs, e.g. interests, knowledge, but also their collaborative characteristics. It is based on the Group Technology ap...

Instant intercommunion techniques such as Instant Messaging (IM) are widely popularized. Aiming at such kind of large scale masscommunication media, clustering on its text content is a practical method to analyze the characteristic of text content in instant messages, and find or track the social hot topics. However, key words in one instant message usually are few, even latent; moreover, sing...

Subphonetic discovery through segmental clustering is a central step in building a corpus-based synthesizer. To help decide what clustering algorithm to use we employed mergeand-split tests on English fricatives. Compared to reference of 2%, Gaussian EM achieved a misclassification rate of 6%, Kmeans 10%, while predictive CART trees performed poorly.

Introduction. The aim of this study is to gather knowledge about how different groups of Icelanders take advantage of information about health and lifestyle in their everyday life. Method. A random sample of 1,000 people was used in the study and data was gathered as a postal survey. Response rate was 50.8%. Analysis. K-means cluster analysis was used to draw four clusters based on the particip...