Breast cancer has increased decidedly among women. But with early diagnosis, a positive response to treatment can be given. Researchers are conducting various studies in imaging methods detect the disease and accurately. In this study, 9 cancerous images taken from TCİA image data bank were detected by K-mean clustering Otsu threshold method. Performance metrics evaluated comparing them marked ...

Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes highest density of in region. algorithms have been effectively used for denoising, seeking, and finding number clusters dataset an automated fashion. However, merits mean quickly fade away as dimensions increase only handful features contain useful information about cluster structure da...

The accurate localization of S1 and S2 is essential for heart sound segmentation classification. However, current direct algorithms have poor noise immunity low accuracy. Therefore, this paper proposes a new optimal algorithm based on K-means clustering Haar wavelet transform. includes three parts. Firstly, method uses the Viola integral Shannon’s energy-based to extract function envelope energ...

Clustering is very helpful in the analysis of raw data. As the clustering is unsupervised learning technique so in order to optimize the results we use classifier with clustering. This paper proposes hybrid technique in which weighted k mean is used for clustering whereas neural networks and SVM are used as classifiers .Weighted k mean results in unlabelled data by calculating the feature weigh...

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...