A New Algorithm for Discovery Maximal Frequent Itemsets Based on Binary Vector Sets

2006 ◽  
Author(s):  
Jing-Wei Xin ◽  
Guo-Qiang Yang ◽  
Ji-Zhou Sun ◽  
Ya-Ping Zhang
Keyword(s):  
Binary Vector ◽  
Frequent Itemsets ◽  
Maximal Frequent Itemsets

New Policy of Maximal Frequent Itemsets in Data Stream Mining

2010 ◽  
Vol 26-28 ◽  
pp. 118-122
Author(s):  
Chong Huan Xu ◽  
Chun Hua Ju
Keyword(s):  
Stream Flow ◽  
Data Stream ◽  
Search Space ◽  
Frequent Itemsets ◽  
Stream Flows ◽  
Algorithm Construct ◽  
Maximal Frequent Itemsets ◽  
Core Idea ◽  
Basic Window ◽  
Pruning Techniques

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

Efficiently Mining Maximal Frequent Itemsets Based on Digraph

2014 ◽  
Vol 610 ◽  
pp. 291-295
Author(s):  
Qiang Wu ◽  
Ding We Wu ◽  
Qin Wang ◽  
Shao Min Wen ◽  
Rong Tu
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Maximal Frequent Itemsets ◽  
Novel Algorithm

In this paper, a novel algorithm for mining maximal frequent itemsets is presented, which has a pre-processing phase where a digraph is constructed. The digraph represents the frequent 2-itemsets which play an important role on the performance of data mining. Then the search for maximal frequent itemsets is done in the digraph. Experiments show that the proposed algorithm is efficient for all types of data.

An Intrusion Detection Model Based on Mining Maximal Frequent Itemsets over Data Streams

2013 ◽  
Vol 339 ◽  
pp. 341-348
Author(s):  
Yi Min Mao ◽  
Xiao Fang Xue ◽  
Jin Qing Chen
Keyword(s):  
Intrusion Detection ◽  
Data Streams ◽  
Intrusion Detection System ◽  
Behavior Pattern ◽  
Detection System ◽  
Response Speed ◽  
Frequent Itemsets ◽  
Abnormal Behavior ◽  
Data Set ◽  
Maximal Frequent Itemsets

Ming association rules have been proved as an important method to detect intrusions. To improve response speed and detecting precision in the current intrusion detection system, this papers proposes an intrusion detection system model of MMFIID-DS. Firstly, to improve response speed of the system by greatly reducing search space, various pruning strategies are proposed to mine the maximal frequent itemsets on trained normal data set, abnormal data set and current data streams to establish normal and abnormal behavior pattern as well as user behavior pattern of the system. Besides, to improve detection precision of the system, misuse detection and anomaly detection techniques are combined. Both theoretical and experimental results indicate that the MMFIID-DS intrusion detection system is fairly sound in performance.

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