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...

This paper proposed a method of audio event chage detection and clustering in movies. Three steps criterion method is used to detect audio event change in movies ,non silece segment is gotton from audio events by using energy firstly, potential audio event change point is gotton by calculating the distance of two sliding windows secondly , penalty distance is used to judge whether a potential a...

The ability to measure objectively wound healing is important for an effective wound management. Describing wound tissues in terms of percentages of each tissue colour is an approved clinical method of wound assessment. Wound healing is indicated by the growth of the red granulation tissue, which is rich in small blood capillaries that contain haemoglobin pigment reflecting the red colour of th...

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...