Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day to day applications.Weather forecasting of this...

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper al...

A novel picture of the relative positions of countries in the world of science is offered through application of a two-dimensional mapping method which is based on quantity and quality indicators of the scientific production as peer-reviewed articles. To obtain such indicators, different influential effects such as the background global trends, temporal fluctuations, disciplinary characteristic...

This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effec...

For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distance-based classification or clustering methods. In addition to this theoretical...

In scientific cloud workflows, large amounts of application data need to be stored in distributed data centres. To effectively store these data, a data manager must intelligently select data centres in which these data will reside. This is, however, not the case for data which must have a fixed location. When one task needs several datasets located in different data centres, the movement of lar...

Because in the traffic video image processing, the background image gotten from background modeling by traditional k-means clustering algorithm shows a lot of noises, thus the improvement of k-means clustering algorithm is proposed, and has been applied to the vehicle flow detection of traffic video image. By analyzing the vehicle detection method and comparing the flow detection algorithm, the...

Data Analysis plays an indispensable role for understanding various phenomena. Clustering algorithms are a class of important tools for data analysis. K-means cluster analysis is considered to cluster protein variates across 3 species using SPSS 16.0. In this Paper we describe an approach to kmeans cluster analysis which grouped the sample data of the three species under study into four apriori...

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...

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...