Among numerous clustering algorithms, clustering by fast search and find of density peaks (DPC) is favoured because it is less affected by shapes and density structures of the data set. However, DPC still shows some limitations in clustering of data set with heterogeneity clusters and easily makes mistakes in assignment of remaining points. The new algorithm, density peak clustering based on relative density optimization (RDO-DPC), is proposed to settle these problems and try obtaining better results. With the help of neighborhood information of sample points, the proposed algorithm defines relative density of the sample data and searches and recognizes density peaks of the nonhomogeneous distribution as cluster centers. A new assignment strategy is proposed to solve the abundance classification problem. The experiments on synthetic and real data sets show good performance of the proposed algorithm.
As one of the typical clustering algorithms, heuristic clustering is characterized by its flexibility in feature integration. This paper proposes a type of heuristic algorithm based on cognitive feature integration. The proposed algorithm employs nonparameter density estimation and maximum likelihood estimation to integrate whole and local cognitive features and finally outputs satisfying clustering results. The new approach possesses great expansibility, which enables priors supplement and misclassification adjusting during clustering process. The advantages of the new approach are as follows: (1) it is effective in recognizing stable clustering results without priors given in advance; (2) it can be applied in complex data sets and is not restricted by density and shape of the clusters; and (3) it is effective in noise and outlier recognition, which does not need elimination of noises and outliers in advance. The experiments on synthetic and real data sets exhibit better performance of the new algorithm.
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