7427813
Triangulation of Bayesian Networks Using an Adaptive Genetic Algorithm
The search for an optimal node elimination sequence for the triangulation of Bayesian networks is an NP-hard problem. In this paper, a new method, called the TAGA algorithm, is proposed to search for the optimal node elimination sequence. TAGA adjusts the probabilities of crossover and mutation operators by itself, and provides an adaptive ranking-based selection operator that adjusts the pressure of selection according to the evolution of the population. Therefore the algorithm not only maintains the diversity of the population and avoids premature convergence, but also improves on-line and off-line performances. Experimental results show that the TAGA algorithm outperforms a simple genetic algorithm, an existing adaptive genetic algorithm, and simulated annealing on three Bayesian networks.
27939024
Junction Tree Factored Particle Inference Algorithm for Multi-Agent Dynamic Influence Diagrams
As MMDPs are difficult to represent structural relations among Agents and MAIDs can not model dynamic environment, we present Multi-Agent dynamic influences (MADIDs). MADIDs have stronger knowledge representation ability and MADIDs may efficiently model dynamic environment and structural relations among Agents. Based on the hierarchical decomposition of MADIDs, a junction tree factored particle filter (JFP) algorithm is presented by combing the advantages of the junction trees and particle filter. JFP algorithm converts the distribution of MADIDs into the local factorial form, and the inference is performed by factor particle of propagation on the junction tree. Finally, and the results of algorithm comparison show that the error of JFP algorithm is obviously less than BK algorithm and PF algorithm without the loss of time performance.
31706841
A Fast Subspace Clustering Algorithm Based on Pattern Similarity
Traditional clustering models define similarity by distance over dimensions. However, distance functions are not always adequate in capturing correlations among the objects. Pattern-based clustering can discover this kind of clusters. But state-of-the-art pattern-based clustering methods are inefficient and haven't criteria to evaluate the quality of clusters. This paper presents a novel pattern similarity-based subspace clustering with the pattern tree (PPSC for short) that offers these capabilities. The method uses new evaluation criteria to discover best clusters, which enables user to find clusters according to different needs. Meanwhile, observing the analogy between mining frequent itemsets and discovering subspace clusters around random points, we apply the pattern-tree to determine subspace by scanning the database once, so it can perform efficiently in large datasets.
45158244
Discovering pattern-based subspace clusters by pattern tree
Traditional clustering models based on distance similarity are not always effective in capturing correlation among data objects, while pattern-based clustering can do well in identifying correlation hidden among data objects. However, the state-of-the-art pattern-based clustering methods are inefficient and provide no metric to measure the clustering quality. This paper presents a new pattern-based subspace clustering method, which can tackle the problems mentioned above. Observing the analogy between mining frequent itemsets and discovering subspace clusters, we apply pattern tree - a structure used in frequent itemsets mining to determining the target subspaces by scanning the database once, which can be done efficiently in large datasets. Furthermore, we introduce a general clustering quality evaluation model to guide the identifying of meaningful clusters. The proposed new method enables the users to set flexibly proper quality-control parameters to meet different needs. Experimental results on synthetic and real datasets show that our method outperforms the existing methods in both efficiency and effectiveness.
8487669
An SMR based advance resource reservation scheme for combined mobility and QoS Provisioning
One of the major problems of deploying RSVP in the mobile environment is called the advance resource reservation problem. If an RSVP reservation path is reserved in advance in the subnet that a mobile node will visit, the mobile node can continue its QoS session smoothly when it hands over to that subnet. However, if too many network resources are used for advance reservation, new QoS sessions originating from that subnet will experience a higher probability of being blocked needlessly. In this paper, we propose a new advance resource reservation scheme that properly constrains the amount of advance reservations in a subnet and only allows the mobile nodes with large value of session-to-mobility ratio (SMR) to make advance reservation. We evaluate the proposed scheme and the results show that our scheme can effectively reduce both active and passive reservation blocking probabilities and achieves a better utilisation of the network resources, especially when the traffic intensity is high.
16346838
Ant-MST: An Ant-Based Minimum Spanning Tree for Gene Expression Data Clustering
We have proposed an ant-based clustering algorithm for document clustering based on the travelling salesperson scenario. In this paper, we presented an approach called Ant-MST for gene expression data clustering based on both ant-based clustering and minimum spanning trees (MST). The ant-based clustering algorithm is firstly used to construct a fully connected network of nodes. Each node represents one gene, and every edge is associated with a certain level of pheromone intensity describing the co-expression level between two genes. Then MST is used to break the linkages in order to generate clusters. Comparing to other MST-based clustering approaches, our proposed method uses pheromone intensity to measure the similarity between two genes instead of using Euclidean distance or correlation distance. Pheromone intensities associated with every edge in a fully-connected network records the collective memory of the ants. Self-organizing behavior could be easily discovered through pheromone intensities. Experimental results on three gene expression datasets show that our approach in general outperforms the classical clustering methods such as K-means and agglomerate hierarchical clustering.
27202859
Affective interaction based on person-independent facial expression space
This paper proposes a person-independent facial expression space (PIFES) to analyze and synthesize facial expressions based on supervised locality preserving projections (SLPP), which aligns different subjects and different intensities of facial expressions on one generalized expression manifold. Interactive curves of different patterns are generated according to the input facial expression image sequence, and target responsive expression images are synthesized for different emotions. In order to synthesize arbitrary expressions for a new person with natural details, a novel approach based on local geometry preserving between the input face image and the target expression image is proposed. Experimental results clearly demonstrate the efficiency of the proposed algorithm.
23849852
Radius Restrained Distributed Breadth First Search Flooding in Large-Scale Multimedia Communication
This paper proposed an algorithm of radius restrained distributed breadth first search flooding for call routing in multimedia communication. By knowing the information of neighbor nodes in finite scope, this algorithm does breadth first search and selects the least number of next forwarding neighbor nodes to reduce redundant information in broadcasting routing information. Simulation results demonstrate that the proposed algorithm lessens the average forwarding times and shortens the delay in transferring routing information. Especially, it can decrease the redundant information to the greatest extent in large scale multimedia communication.
40601169
Performance Modeling and Practical Parallel Algorithms of Cluster Computing with Applications on Distributed Platforms