32406655
A Parallel Algorithm for Learning Bayesian Networks
Computing the expected statistics is the main bottleneck in learning Bayesian networks in large-scale problem domains. This paper presents a parallel learning algorithm, PL-SEM, for learning Bayesian networks, based on an existing structural EM algorithm (SEM). Since the computation of the expected statistics is in the parametric learning part of the SEM algorithm, PLSEM exploits a parallel EM algorithm to compute the expected statistics. The parallel EM algorithm parallelizes the E-step and M-step. At the E-step, PLSEM parallel computes the expected statistics of each sample; and at the M-step, with the conditional independence of Bayesian networks and the expected statistics computed at the E-step, PL-SEM exploits the decomposition property of the likelihood function under the completed data to parallel estimate each local likelihood function. PL-SEM effectively computes the expected statistics, and greatly reduces the time complexity of learning 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.
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.
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.
694386
A Data Storage Mechanism for P2P VoD Based on Multi-channel Overlay
It is a big challenge to provide Video-on-Demand streaming services over Internet in a scalable way. Currently, many researchers use a single channel overlay to implement the scalability of on-demand streaming services. However, in a real application environment, various channels in a P2P VOD system have different popularities, which probably cause the imbalance of data storage-capability of the whole system. It results in a problem that a mass of unpopular channels' caching capability can not be used to satisfy the data requirements of the whole system. In order to solve the problem, this paper proposes a new data-storage mechanism, which constructs a multi-channel overlay to optimize the whole system's caching-capability and greatly improves unpopular channel's caching efficiency. The experimental results show that this mechanism can achieve significant effects.
8066461
Scalability of Hybrid Fuzzy C-Means Algorithm Based on Quantum-Behaved PSO
A new hybrid fuzzy clustering algorithm that incorporates the fuzzy c-means (FCM) into the quantum-behaved particle swarm optimization (QPSO) algorithm is proposed in this paper (QPSO+FCM). The QPSO has less parameters and higher convergent capability of the global optimizing than particle swarm optimization algorithm (PSO). So the iteration algorithm is replaced by the QPSO based on the gradient descent of FCM, which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM and in a large degree avoids depending on the initialization values. This paper also investigates the ability of FCM algorithm, PSO+FCM algorithm and GA+FCM algorithm with Iris testing data and Wine testing data. The simulation result proves that compared with other algorithms, the new algorithm not only has the favorable convergence but also has been obviously improved the clustering effect.
11024065
An algorithm for optimal comma free codes with isomorphism rejection
A general algorithm for finding optimal comma-free codes and deriving upper bounds on the minimum redundancy of comma-free codes that implements the idea of isomorphism rejection is presented, together with tables with bounds on the minimum redundancy computed by using this algorithm.
22165485
An Efficient Fuzzy Kohonen Clustering Network Algorithm
Fuzzy Kohonen clustering networks (FKCN) are well known for clustering analysis (unsupervised learning and self-organizing). This classification of FKCN algorithm is a set of iterative procedures that suffer some major problems, for example its constringency rate is not too fast for a large amount of datasets. To overcome these defects, an efficient fuzzy Kohonen network algorithm is proposed in this paper, which can significantly reduce the computation time required to partition a dataset into desired clusters. By introducing the threshold values and fuzzy convergence operators in the network learning procedure to adjust the learning rates dynamically, the network convergence rate is greatly improved and the error rates of dataset cluster are significantly decreased. Experimental results show the new algorithm is on average three times faster than the original FKCN algorithm. We also demonstrate that the quality of the improved FKCN is better than the original FKCN algorithm.
9203049
A New Theory of Element and Number