The Experts below are selected from a list of 9225 Experts worldwide ranked by ideXlab platform
Mohammed J Zaki - One of the best experts on this subject based on the ideXlab platform.
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musk uniform sampling of k maximal Patterns
SIAM International Conference on Data Mining, 2009Co-Authors: Mohammad Al Hasan, Mohammed J ZakiAbstract:Recent research in Frequent Pattern Mining (FPM) has shifted from obtaining the complete set of Frequent Patterns to generating only a representative (summary) subset of Frequent Patterns. Most of the existing approaches to this problem adopt a two-step solution; in the first step, they obtain all the Frequent Patterns, and in the second step, some form of clustering is used to obtain the summary Pattern set. However, the twostep method is inefficient and sometimes infeasible since the first step itself may fail to finish in a reasonable amount of time. In this paper, we propose an alternative approach to Mining Frequent Pattern representatives based on a uniform sampling of the output space. Our new algorithm, Musk, obtains representative Patterns by sampling uniformly from the pool of all Frequent maximal Patterns; uniformity is achieved by a variant of Markov Chain Monte Carlo (MCMC) algorithm. Musk simulates a random walk on the Frequent Pattern partial order graph with a prescribed transition probability matrix, whose values are computed locally during the simulation. In the stationary distribution of the random walk, all maximal Frequent Pattern nodes in the partial order graph are sampled uniformly. Experiments on various kind of graph and itemset databases validate the effectiveness of our approach.
Liangyu Ouyang - One of the best experts on this subject based on the ideXlab platform.
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the studies of Mining Frequent Patterns based on Frequent Pattern tree
Knowledge Discovery and Data Mining, 2009Co-Authors: Showjane Yen, Yueshi Lee, Chiukuang Wang, Liangyu OuyangAbstract:Mining Frequent Patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for Mining Frequent Pattern. However, either the search space or memory space is huge, such that the performance for the previous approach degrades when the database is massive or the threshold for Mining Frequent Patterns is low. In order to decrease the usage of memory space and speed up the Mining process, we study some methods for Mining Frequent Patterns based on Frequent Pattern tree. The concept of our approach is to only construct a FP-tree and traverse a subtree of the FP-tree to generate all the Frequent Patterns for an item without constructing any other subtrees. After traversing a subtree for an item, our approach merges and removes the subtree to reduce the FP-tree smaller and smaller. We propose four methods based on this concept and compare the four methods with the famous algorithm FP-Growth which also construct a FP-tree and recursively mines Frequent Patterns by building conditional FP-tree.
Wenbin Zhan - One of the best experts on this subject based on the ideXlab platform.
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hybrid reliability parameter selection method based on text Mining Frequent Pattern growth algorithm and fuzzy bayesian network
Journal of Shanghai Jiaotong University (science), 2018Co-Authors: Yong Shuai, Tailiang Song, Jianping Wang, Wenbin ZhanAbstract:Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text Mining is used to extract the feature and curtail feature sets from text data firstly, and Frequent Pattern tree (FPT) of the text data is constructed to reason Frequent item-set between the key factors by Frequent patter growth (FPG) algorithm. Then on the basis of fuzzy Bayesian network (FBN) and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in Frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.
Mohammad Al Hasan - One of the best experts on this subject based on the ideXlab platform.
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musk uniform sampling of k maximal Patterns
SIAM International Conference on Data Mining, 2009Co-Authors: Mohammad Al Hasan, Mohammed J ZakiAbstract:Recent research in Frequent Pattern Mining (FPM) has shifted from obtaining the complete set of Frequent Patterns to generating only a representative (summary) subset of Frequent Patterns. Most of the existing approaches to this problem adopt a two-step solution; in the first step, they obtain all the Frequent Patterns, and in the second step, some form of clustering is used to obtain the summary Pattern set. However, the twostep method is inefficient and sometimes infeasible since the first step itself may fail to finish in a reasonable amount of time. In this paper, we propose an alternative approach to Mining Frequent Pattern representatives based on a uniform sampling of the output space. Our new algorithm, Musk, obtains representative Patterns by sampling uniformly from the pool of all Frequent maximal Patterns; uniformity is achieved by a variant of Markov Chain Monte Carlo (MCMC) algorithm. Musk simulates a random walk on the Frequent Pattern partial order graph with a prescribed transition probability matrix, whose values are computed locally during the simulation. In the stationary distribution of the random walk, all maximal Frequent Pattern nodes in the partial order graph are sampled uniformly. Experiments on various kind of graph and itemset databases validate the effectiveness of our approach.
Showjane Yen - One of the best experts on this subject based on the ideXlab platform.
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the studies of Mining Frequent Patterns based on Frequent Pattern tree
Knowledge Discovery and Data Mining, 2009Co-Authors: Showjane Yen, Yueshi Lee, Chiukuang Wang, Liangyu OuyangAbstract:Mining Frequent Patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for Mining Frequent Pattern. However, either the search space or memory space is huge, such that the performance for the previous approach degrades when the database is massive or the threshold for Mining Frequent Patterns is low. In order to decrease the usage of memory space and speed up the Mining process, we study some methods for Mining Frequent Patterns based on Frequent Pattern tree. The concept of our approach is to only construct a FP-tree and traverse a subtree of the FP-tree to generate all the Frequent Patterns for an item without constructing any other subtrees. After traversing a subtree for an item, our approach merges and removes the subtree to reduce the FP-tree smaller and smaller. We propose four methods based on this concept and compare the four methods with the famous algorithm FP-Growth which also construct a FP-tree and recursively mines Frequent Patterns by building conditional FP-tree.