Wi-Fi fingerprint acquisition and modeling is a time-consuming work, while crowdsourcing is an effective way to solve this problem. The feasibility of unsupervised clustering was demonstrated for Place of Interest (POI) positioning, which is benefit to generate radio map by crowded source. At first, a framework of Wi-Fi fingerprint localization algorithm was given, then the k-means, affinity propagation and adaptive propagation were applied to this framework. Using BP neural network as a supervised learning reference, an evaluation was executed in a laboratory to analyze the relationship between indoor POI partition and spatial division, and the Radio Signal Strength Indications (RSSI) were collected in POI. Compared the clustering results in the POI spatial space, the recall and the precision of the three clustering algorithms were both over 90%. The experimental results show that the unsupervised clustering method is an effective solution for coarse-grained POI indoor positioning application.

In order to improve the processing ability for uncertainty data using the traditional Fuzzy Support Vector Machine (FSVM), FSVM with fuzzy similarity measure and high dimensional space fuzzy mapping was proposed. Firstly, by using Gregson similarity measure, the fuzzy similarity measure function was established, which was effective to explain the uncertainty information. And then, using the theory of mapping and Mercer, fuzzy similarity kernel learning was formulated and used in the algorithm of the FSVM. Finally, this algorithm was used to the modeling of the material removal rate in the rotary ultrasonic machining with uncertainty data. Compared to the results using traditional FSVM methods, the current approach can better process uncertainty data with less operation steps. And the proposed method has higher accuracy in processing uncertainty data with lower computational complexity.