We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without s...

Finding discords in time series database is an important problem in the last decade due to its variety of real-world applications, including data cleansing, fault diagnostics, and financial data analysis. The best known approach to our knowledge is HOT SAX technique based on the equiprobable distribution of SAX representations of time series. This characteristic, however, is not preserved in th...

the wisdom of crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. this theory used for in clustering problems. previous researches showed that this theory can significantly increase the stability and performance of lea...

Lung cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis can be very helpful for successful treatment. Image segmentation plays a key role in the early detection and diagnosis of lung cancer. K-means algorithm and classic PSO clustering are the most common methods for segmentation that have poor outputs. In t...

This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of ...

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 article gives a constant factor approximation algorithm for streaming k-means that usesO(k log n) space.

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...