Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certain amount of outlying observations in the input data of many practical applications such as biological sequenc...Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certain amount of outlying observations in the input data of many practical applications such as biological sequences analysis or gene expressions analysis. This paper presents a fuzzy clustering algorithm based on average link and possibilistic clustering paradigm termed as AVLINK. It minimizes the average dissimilarity between pairs of patterns within the same cluster and at the same time the size of a cluster is maximized by computing the zeros of the derivative of proposed objective function. AVLINK along with the proposed initialization procedure show a high outliers rejection capability as it makes their membership very low furthermore it does not requires the number of clusters to be known in advance and it can discover clusters of non convex shape. The effectiveness and robustness of the proposed algorithms have been demonstrated on different types of protein data sets.展开更多
Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly ...Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly extract the sample entropy of mixed signal, mean and variance to calculate each signal sample entropy, finally uses the K mean clustering to recognize. The simulation results show that： the recognition rate can be increased to 89.2% based on sample entropy.展开更多
文摘Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certain amount of outlying observations in the input data of many practical applications such as biological sequences analysis or gene expressions analysis. This paper presents a fuzzy clustering algorithm based on average link and possibilistic clustering paradigm termed as AVLINK. It minimizes the average dissimilarity between pairs of patterns within the same cluster and at the same time the size of a cluster is maximized by computing the zeros of the derivative of proposed objective function. AVLINK along with the proposed initialization procedure show a high outliers rejection capability as it makes their membership very low furthermore it does not requires the number of clusters to be known in advance and it can discover clusters of non convex shape. The effectiveness and robustness of the proposed algorithms have been demonstrated on different types of protein data sets.
文摘Sample entropy can reflect the change of level of new information in signal sequence as well as the size of the new information. Based on the sample entropy as the features of speech classification, the paper firstly extract the sample entropy of mixed signal, mean and variance to calculate each signal sample entropy, finally uses the K mean clustering to recognize. The simulation results show that： the recognition rate can be increased to 89.2% based on sample entropy.
