In this paper, we explored how to use meta-data information in information retrieval task. We presented a new language model that is able to take advantage of the category information for documents to improve the retrieval accuracy. We compared the new language model with the traditional language model over the TREC4 dataset where the collection information for documents is obtained using the k...

At present, there are many online learning systems which are available and provided on the Internet. Almost all of these systems are static contents and test banks. To improve the teaching and learning on the Internet, this study proposes a new learning system that can be adjusted to the knowledge and understanding of students. This system uses K-Means Algorithm to cluster the student by pretes...

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at...

An optimal data partitioning in parallel/distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd-Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimen...

Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDDmethod using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed subproblems to get support vectors and retrains with the suppo...

In this paper two techniques for units clustering and factorial dimensionality reduction of variables and occasions of a three-mode data set are discussed. These techniques can be seen as the simultaneous version of two procedures based on the sequential application of k-means and Tucker2 algorithms and vice versa. The two techniques, T3Clus and 3Fk-means, have been compared theoretically and e...

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