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 growth in interest in RGB-D devices (e.g. Microsoft Kinect or ASUS Xtion Pro) is based on their low price, as well as the wide range of possible applications. These devices can provide skeletal data consisting of 3D position, as well as orientation data, which can be further used for pose or action recognition. Data for 15 or 20 joints can be retrieved, depending on the libraries used. Rece...

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...

This paper investigates scalable implementations of out-ofcore I/O-intensive Data Mining algorithms on a ordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.

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...

This study conducts the K-means grouping analysis on 1,373 Facebook pages in order to find the difference and characteristics between groups, and furthermore attempt to understand the behavioural characteristics of Facebook page users. The study produces four clusters with different characteristics, all of which are named and defined according to their qualities. The four types of pages are the...