Clustering is an important task in mining the evolving data streams. A lot of data streams are high dimensional in nature. Clustering in the high dimensional data space is a complex problem, which is inherently more complex for data streams. Most data stream clustering methods are not capable of dealing with high dimensional data streams; therefore they sacrifice the accuracy of clusters. In or...

Large-scale data-centric services are often handled by clusters of computers that include hundreds of thousands of computing nodes. However, traditional distributed query processing techniques fail to handle the large-scale distribution, peer-to-peer communication and frequent disconnection. In this paper, we introduce LOT, a robust, fault-tolerant and highly distributed overlay network for lar...

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applic...

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster centers. It calculates the entropy at ea...

One of the fundamental steps in the transformation of the LIDAR data into the meaningful objects in urban area involves their segmentation into consistent units through a clustering process. Nevertheless, due to the scene complexity and the variety of objects in urban area, e.g. buildings, roads, and trees, it is clear that a clustering using only a single cue will not suffice. Considering the ...

Han, Sangchun PhD, Purdue University, December 2014. A Method for Clustering High-Dimensional Data Using 1D Random Projections. Major Professor: Mireille Boutin. Clustering high-dimensional data is more difficult than clustering low-dimensional data. The problem is twofold. First, there is an efficiency problem related to the data size, which increases with the dimensionality. Second, there is ...