207717559
Associative Clustering for Clusters of Arbitrary Distribution Shapes
A novel neural network, named Associative Clustering Neural Network (ACNN), is developed for clustering data whose underlying distribution shapes are arbitrary. ACNN is a dynamic model that collectively measures and updates the similarity of any two patterns through the interaction of a group of patterns. Such a new measure of similarity helps to achieve more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The efficience of ACNN has been verified through the performance study.
44509988
A threshold criterion, auto-detection and its use in MST-based clustering
Clustering is to group data points into homogenous clusters so that data points within the same cluster are more similar than data points belonging to different clusters. There are many effective clustering algorithms for discovering arbitrary shaped clusters, but one common problem of many algorithms is the difficulty for users to decide appropriate parameters for these algorithms. To reduce the dependence of clustering performance on parameters, this paper proposes a threshold criterion for the single linkage cluster analysis and incorporates it into the Minimum Spanning Tree (MST) based clustering method. Since the threshold can be automatically decided according to the underlying data distributions, arbitrary shaped clusters can be discovered with little human intervention. The experimental results on spatial data are very encouraging.
27714693
Efficient Parallel Processing for K-Nearest-Neighbor Search in Spatial Databases
Even though the problem of k nearest neighbor (kNN) query is well-studied in serial environment, there is little prior work on parallel kNN search processing in parallel one. In this paper, we present the first Best-First based Parallel kNN (BFPkNN) query algorithm in a multi-disk setting, for efficient handling of kNN retrieval with arbitrary values of k by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFPkNN significantly outperforms its competitors in both efficiency and scalability.
4775385
A fine-grained replacement strategy for XML query cache
Caching popular queries and reusing results of previously computed queries is one important query optimization technique, especially in modern distributed environments such as the WWW. Based on the recent proliferation of XML data and the emergence of the XQuery language, we are thus developing a query- based caching system for XQuery queries, called ACE-XQ. ACE-XQ applies innovative query containment and rewriting strategies to answer incoming user queries based on the cached XQueries, whenever possible, instead of accessing remote XML data sources.To manage the space of the cache, a straightforward application of traditional replacement strategies would correspond to removing a complete cached query and its derived XML document as a whole when space needs to be freed. This coarse granularity however does not match well with the typical access pattern of web searches where new queries often partially overlap with cached queries.In this paper, we propose a novel replacement strategy appropriate for such query-based XML caching systems. In particular, we collect user access statistics at the granularity of the XML path structure instead of the complete XML query regions. We then apply a more fine-grained replacement strategy that purges XML fragments off a cached region instead of the whole XML document and accordingly adjusts the query descriptor. This may better capture the user access patterns since more frequently used XML document fragments are likely to remain in the cache while other less beneficial parts are purged. This approach has been implemented in our ACE-XQ System. Preliminary experiment results illustrate the performance improvement achievable by our fine-grained replacement strategy over the one which replaces a whole XML view at a time when the cache size is relatively large.
441449
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
60542101
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
2673173
State Aware Data Dissemination over Structured Overlays
We describe the problem of data dissemination in stream-oriented applications where the required filter is a function of the current state. We call such functions dynamic filters. A state aware data dissemination network (SA-DDN) is proposed to support dynamic filters. Two approaches single-level filtering (SF) and multilevel filter decomposition (MFD) are proposed to facilitate the data dissemination. We show how MFD improves performance over SF. We then describe a realization of SA-DDN on top of an improved bi-directional Chord overlay with a built-in multicast mechanism. An application of stock price monitoring is implemented based on SA-DDN and real life stock quotes are collected to demonstrate the feasibility of our system. Extensive simulations are performed to compare the performance of both approaches and provide insight into the advantages of MFD