This paper investigates the validity of Kleinberg’s axioms for clustering functions with respect to the quite popular clustering algorithm called k-means.We suggest that the reason why this algorithm does not fit Kleinberg’s axiomatic system stems from missing match between informal intuitions and formal formulations of the axioms. While Kleinberg’s axioms have been discussed heavily in the pas...

Learning style refers to an individual’s approach to learning based on his or her preferences, strengths, and weaknesses. Problem solving is considered an essential cognitive activity wherein people are required to understand a problem, apply their knowledge, and monitor behavior to solve the issue. Problem solving has recently gained attention in education research, as it is considered an esse...

Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamics. It aims at reducing risk in market capital investments. Grouping stocks by similar latent trend can be cast into a clustering problem. The classical K-Means clustering algorithm does not fit the task of financial data analysis. Hence, we investigate Non-negative Matrix Factorization (NMF) techniq...

The performance of K-means and Gaussian mixture model (GMM) clustering depends on the initial guess of partitions. Typically, clus∗corresponding author

Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or n...

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. To make use of this information, in this paper, we develop a new clustering method “MLPKMEANS” by combining Multi-Layer Perceptron and K-means. We test our method on several data sets with partial c...

This paper discusses a new approach for clustering musical bass-line patterns representing particular genres and its application to audio genre classification. Many musical genres are characterized not only by timbral information but also by distinct representative bass-line patterns. So far this kind of temporal features have not so effectively been utilized. In particular, modern music songs ...

In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effect...

Performance of iterative clustering algorithms which converges to numerous local minima depend highly on initial cluster centers. Generally initial cluster centers are selected randomly. In this paper we propose an algorithm to compute initial cluster centers for K-means clustering. This algorithm is based on two observations that some of the patterns are very similar to each other and that is ...

It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boos...