Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximu...

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...

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.

Time series have become an important class of temporal data objects in our daily life while clustering analysis is an effective tool in the fields of data mining. However, the validity of clustering time series subsequences has been thrown into doubts recently by Keogh et al. In this work, we review this problem and propose the phase shift weighted spherical k-means algorithm (PS-WSKM in abbrev...

Although all university majors are prominent, and the necessity of their presence is of no question, they might not have the same priority basis considering different resources and strategies that could be spotted for a country. Their priorities likely change as the time goes by; that is, different majors are desirable at different time. If the government is informed of which majors could tackl...

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...

Clustering is a data mining activity that aims to differentiate groups inside a given set of objects, with respect to a set of relevant attributes of the analyzed objects. Generally, existing clustering methods, such as k-means algorithm, start with a known set of objects, measured against a known set of attributes. But there are numerous applications where the attribute set characterizing the ...

This paper presents exploratory results on how a data-mining-based tool can be used to enhance the quality of decision-making in a Virtual Enterprise environment. The developed tool is based on the Clustering mining method and implements the K-Means algorithm. The algorithm is explained, its utilization in the proposed model is introduced and the implementation results are presented and stresse...

Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies– Bouldin In...

In this paper we present a model to predict the stock trend based on a combination of sequential chart pattern, K-means and AprioriAll algorithm. The stock price sequence is truncated to charts by sliding window, then the charts are clustered by K-means algorithm to form chart patterns. Therefore, the chart sequences are converted to chart pattern sequences, and frequent patterns in the sequenc...