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 ...

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Cardiac fibre architecture plays a key role in heart function. Recently, the estimation of fibre structure has been simplified with diffusion tensor MRI (DT-MRI). In order to assess the heart architecture and its underlying function, with the goal of dealing with pathological tissues and easing inter-patient comparisons, we propose a methodology for finding cardiac myofibrille trace corresponde...

Clustering is one of the important data mining techniques. k-Means [1] is one of the most important algorithm for Clustering. Traditional k-Means algorithm selects initial centroids randomly and in k-Means algorithm result of clustering highly depends on selection of initial centroids. k-Means algorithm is sensitive to initial centroids so proper selection of initial centroids is necessary. Thi...

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

in this paper, the notion of the digital divide has been described, and a few analyzing methods of digital divide have been reviewed. analyzing methods of digital divide are called indices which have different indicators and different formulas for calculation. since data collection for an indicator may be difficult, calculating an index is an essential problem. we collected and calculated some ...

Aiming at the problems of traditional K-means clustering algorithm, such as local optimal solution and slow speed caused by uncertainty k value randomness initial cluster center selection, this paper proposes an improved KMeans method. The algorithm first uses idea elbow rule based on sum squares errors to obtain appropriate number clusters k, then variance a measure degree dispersion samples, ...