Due to the recent emergence Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. As the satellite imagery is getting generated at a higher rate than the previous decades, it becomes essential to have better solutions in terms of accuracy as well as performance. In this paper,...

Breast cancer is the most common cancer in women, and is considered one of the most common causes of death. It increases by an alarming rate globally. Earlier detection and diagnosis could save lives and improve quality of life. In this paper, a new method for breast cancer diagnosis is presented. The proposed method, SVM-Kmeans, combines Kmeans, an unsupervised learning clustering technique, w...

With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In or...

Instant intercommunion techniques such as Instant Messaging (IM) are widely popularized. Aiming at such kind of large scale masscommunication media, clustering on its text content is a practical method to analyze the characteristic of text content in instant messages, and find or track the social hot topics. However, key words in one instant message usually are few, even latent; moreover, sing...

In this paper, we propose a new document clustering method based on the K-means method (kmeans). In our method, we allow only finite candidate vectors to be representative vectors of kmeans. We also propose a method for constructing these candidate vectors using documents that have the same word. We participated in NTCIR-4 WEB Task D (Topic Classification Task) and experimentally compared our m...

Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better t...

Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of tasks in the field of Information Retrieval (IR). Clustering has become one of the most act...

In this study, MR Image segmentation has been realized with some clustering algorithms. In the study, the performances kmeans, lloyds, llyds-kmeans, pso clustering, ga clustering and jaya optimisation algorithms on some MR images from BRATS 2012 dataset have been compared. For the comparison, the manual segmentation results of MR images from BRATS 2012 dataset have been referenced and results h...