Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. ...

Mean shift clustering finds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to fix the number of clusters in advance, the mean shift has been a popular clustering algorithm in various application fields. A typical implementation of the mean shift is to first estimate the density by kernel density estimation and then com...

Clustering is an important technique in machine learning, which has been successfully applied in many applications such as text and webpage classifications, but less in transaction database classification. A large organization usually has many branches and accumulates a huge amount of data in their branch databases called multidatabases. At present, the best way of mining multidatabases is, fir...

An enhanced grey clustering analysis method based on accumulation sequences using grey relational analysis (AGRA) is put forward for specifying hierarchies of clusters in panel data. The clustering method can handle panel data containing N samples, each of which hasm time series of indicators for which the observations for a given time series can bemeasured at different times than other series ...

This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically; thirdly, a distance regularized level set is used for refinemen...

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparis...

A 3D rib cage model helps to study anatomical structures in some medical applications such as biomechanical and surgical operations. Its quality directly depends on rib cage segmentation if it is reconstructed from image data. This paper presents an optional segmentation method based on K-means clustering. It uses a hierarchical concept to control the clustering, and it organizes clustered regi...