Selfadaption neighborhood density clustering method for mixed data stream with concept drift Abstract   Clustering analysis is an important data mining method for data stream. In this paper, a selfadaption neighborhood density clustering method for mixed data stream is proposed. The method uses a significant metric criteria to make categorical attribute values become numeric and then the dimension of data is reduced by a nonlinear dimensionality reduction method. In the clustering method, each point is evaluated by neighborhood density. The k points are selected from the data set with maximum mutual distance after k is determined according to rough set. In addition, a new similarity measure based on neighborhood entropy is presented. The data points can be partitioned into the nearest cluster and the algorithm adaptively adjusts the clustering center points by clustering error. The experimental results show that the proposed method can obtain better clustering results than the comparison algorithms on the most data sets and the experimental results prove that the proposed algorithm is effective for data stream clustering.

Evolving clustering algorithm based on mixture of typicalities for stream data mining Abstract   Many applications have been producing streaming data nowadays, which motivates techniques to extract knowledge from such sources. In this sense, the development of data stream clustering algorithms has gained an increasing interest. However, the application of these algorithms in real systems remains a challenge, since data streams often come from nonstationary environments, which can affect the choice of a proper set of model parameters for fitting the data or finding a correct number of clusters. This work proposes an evolving clustering algorithm based on a mixture of typicalities. It is based on the TEDA framework and divide the clustering problem into two subproblems: microclusters and macroclusters. Experimental results with benchmarking data sets showed that the proposed methodology can provide good results for clustering data and estimating its density even in the presence of events that can affect data distribution parameters, such as concept drifts. In addition, the model parameters were robust in relation to the stateoftheart algorithms.