Chun-Sheng Chen

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We derive ensembles of decision trees through a nonparametric Bayesian model, allowing us to view random forests as samples from a posterior distribution. This insight provides large gains in interpretability, and motivates a class of Bayesian forest (BF) algorithms that yield small but reliable performance gains. Based on the BF framework, we are able to(More)
The basic idea of traditional density estimation is to model the overall point density analytically as the sum of influence functions of data points. However, traditional density estimation techniques only consider the location of a point. Supervised density estimation techniques, on the other hand, additionally consider a variable of interest that is(More)
Detecting changes in spatial datasets is important for many fields. In this paper, we introduce a methodology for change analysis in spatial datasets that combines contouring algorithms with supervised density estimation techniques. The methodology allows users to define their own criteria for features of interest and to identify changes in those features(More)
Representative-based clustering algorithms are quite popular due to their relative high speed and because of their sound theoretical foundation. On the other hand, the clusters they can obtain are limited to convex shapes and clustering results are also highly sensitive to initializations. In this paper, a novel agglomerative clustering algorithm called(More)
Adaptive clustering uses external feedback to improve cluster quality; past experience serves to speed up execution time. An adaptive clustering environment is proposed that uses Q-learning to learn the reward values of successive data clusterings. Adaptive clustering supports the reuse of clusterings by memorizing what worked well in the past. It has the(More)
Polygons can serve an important role in the analysis of geo-referenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering(More)
Analyzing change in spatial data is critical for many applications including developing early warning systems that monitor environmental conditions, epidemiology, crime monitoring, and automatic surveillance. In this paper, we present a framework for the detection and analysis of patterns of change; the framework analyzes change by comparing sets of(More)
Data mining is used to extract valuable knowledge from vast pools of data. Due to the computational complexity of the algorithms applied and the problems of handling large data sets themselves, data mining applications often require days to perform their analysis when dealing with large data sets. This paper presents the design and evaluation of a parallel(More)
Analyzing trajectories is important and has many applications, such as surveillance, analyzing tra c patterns and hurricane path prediction. In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes. First, a density-based clustering algorithm DENTRAC that(More)
The maim idea proposed in this paper is integrating sliding mode control (SMC) theory and cerebellar model articulation controller (CMAC) neural network into fuzzy controller design and the fuzzy control rules can be determined systematically by the sliding condition of the SMC. The advantages of using fuzzy model into CMAC are to improve function(More)