In order to solve the problem of selecting center manually in a clustering algorithm, a Parameter-free Clustering Algorithm based on Laplace centrality and density peaks (ALPC) was proposed. Laplace centrality was used to measure the centrality of objects, and a normal distribution probability statistical method was used to determine clustering centers. The problem that clustering algorithms rely on empirical parameters and manually determine cluster centers was solved by the proposed algorithm. Each object was assigned to the corresponding cluster center according to the distance between the object and the center. The experimental results on synthetic data sets and UCI data sets show that the new algorithm can not only automatically determine cluster centers without any priori parameters, but also get better results with higher accuracy compared with the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, Clustering by fast search and find of Density Peaks (DPC) algorithm and Laplace centrality Peaks Clustering (LPC) algorithm.

In order to find density backbone quickly and improve the accuracy of high-dimensional data clustering results, a new algorithm for fast recognition of high-density backbone was put forward, which was named Efficient CLUstering based on density Backbone (ECLUB) algorithm. Firstly, on the basis of defining the local density of object, the high-density backbone was identified quickly according to the mutual consistency of k-nearest neighbors and the local density relation of neighbor points. Then, the unassigned low-density points were divided according to the neighborhood relations to obtain the final clustering. The experimental results on synthetic datasets and real datasets show that the proposed algorithm is effective. The clustering results of Olivetti Face dataset show that, the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) of the proposed ECLUB algorithm is 0.8779 and 0.9622 respectively. Compared with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Clustering by Fast search and find of Density Peaks (CFDP) algorithm and CLUstering based on Backbone (CLUB) algorithm, the proposed ECLUB algorithm is more efficient and has higher clustering accuracy for high-dimensional data.

In multi-view clustering based on view weighting, weight value of each view products great influence on clustering accuracy. Aiming at this problem, a multi-view clustering algorithm named Entropy Weighting Multi-view Kernel K-means (EWKKM) was proposed, which assigned a reasonable weight to each view so as to reduce the influence of noisy or irrelevant views, and then to improve clustering accuracy. In EWKKM, different views were firstly represented by kernel matrix and each view was assigned with one weight. Then, the weight of each view was calculated from the corresponding information entropy. Finally, the weight of each view was optimized according to the defined optimized objective function, then multi-view clustering was conducted by using the kernel K-means method.The experiments were done on the UCI datasets and a real datasets. The experimental results show that the proposed EWKKM is able to assign the optimal weight to each view, and achieve higher clustering accuracy and more stable clustering results than the existing cluster algorithms.

In order to improve the accuracy of the estimated missing data in Wireless Sensor Network (WSN), a self-decision interpolation algorithm was proposed. The algorithm selected different estimation strategies of missing data according to the spatial correlation of the data sets and the continuity of missing data, then introduced the Auto-Regressive and Moving Average (ARMA) model into the study of missing data interpolation. In corresponding to the traditional missing value estimation algorithm, the proposed algorithm not only considered the characteristics of wireless sensor networks, but also took the characteristics of the data themselves into account. The experimental results on the real data sets show that the proposed algorithm improves the precision of the estimation for missing data.