Clustering technology has received a lot of concern in many areas such as engineering, medicine, biology and data mining. Collecting data points is the purpose of clustering and the most common clustering technology is K-means algorithm. However, results of kmeans depend on the initial state and convergence to a local optimum is also its drawback. To overcome these drawbacks, many studies have ...

This paper proposes an improved color barycenter model (CBM) for road sign detection. The previous version of CBM can find out the colors of road-sign (RS), but its accuracy is not high enough for magenta and blue region segmentation. The improved CBM extends the barycenter distribution to cylinder coordinate and takes the number of colors in every point into account. Then the K-means clusterin...

Prior work has shown that generalization of data in an Example Based Machine Translation (EBMT) system, reduces the amount of pre-translated text required to achieve a certain level of accuracy (Brown, 2000). Several word clustering algorithms have been suggested to perform these generalizations, such as kMeans clustering or Group Average Clustering. The hypothesis is that better contextual clu...

Change detection algorithms play a vital role in overseeing the transformations on the earth surface. Unsupervised change detection has a indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc In this paper, a novel method for unsupervised change detectio...

Data clustering is a process of putting similar data into groups. A clustering algorithms partition data set into several groups such that the similarity within a group is larger than among groups. Association rule is one of the possible methods for analysis of data. The association rules algorithm generates a huge number of association rules, of which many are redundant. The main idea of this ...

Application of document clustering techniques to cluster e-mails is an interesting application. Techniques like kmeans, EM etc can be used to achieve this. However, the selection of a good distance metric is the key issue involved. Often people manually tweak the chosen distance metric to achieve desirable/good clusters/results that in all certainty do not provide a generic solution. Hence it w...

Customer clustering is used to understand customers’ preferences and behaviors by examining the differences and similarities between customers. Kohonen vector quantization clustering technology is used in this research and is compared with Kmeans clustering. The data set consists of customer records obtained from a mobile telecommunications service provider. The customers are clustered using va...

A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dim...

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means ...

We consider the problem of clustering large document sets into disjoint groups or clusters. Our starting point is recent literature on effective clustering algorithms, specifically Principal Direction Divisive Partitioning (PDDP), proposed by Boley in [1] and Spherical k-Means (“S–kmeans” for short) proposed by Dhillon and Moda in [4]. In this paper we study and evaluate the performance of thes...