scholarly journals Online Mining (Recently) Maximal Frequent Itemsets over Data Streams

2005 ◽  
Author(s):  
Hua-Fu Li ◽  
Suh-Yin Lee ◽  
Man-Kwan Shan
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Maximal Frequent Itemsets ◽  
Online Mining

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.

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

scholarly journals Mining Recent Maximal Frequent Itemsets Over Data Streams with Sliding Window

2018 ◽  
Vol 16 (6) ◽  
pp. 961-969 ◽  
Author(s):  
Saihua Cai ◽  
Shangbo Hao ◽  
Ruizhi Sun ◽  
Gang Wu
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Memory Usage ◽  
Time Cost ◽  
Improved Method ◽  
Huge Number ◽  
Frequent Itemsets Mining ◽  
Series Of Experiments ◽  
Maximal Frequent Itemsets

Abstract: The huge number of data streams makes it impossible to mine recent frequent itemsets. Due to the maximal frequent itemsets can perfectly imply all the frequent itemsets and the number is much smaller, therefore, the time cost and the memory usage for mining maximal frequent itemsets are much more efficient. This paper proposes an improved method called Recent Maximal Frequent Itemsets Mining (RMFIsM) to mine recent maximal frequent itemsets over data streams with sliding window. The RMFIsM method uses two matrixes to store the information of data streams, the first matrix stores the information of each transaction and the second one stores the frequent 1-itemsets. The frequent p-itemsets are mined with “extension” process of frequent 2-itemsets, and the maximal frequent itemsets are obtained by deleting the sub-itemsets of long frequent itemsets. Finally, the performance of the RMFIsM method is conducted by a series of experiments, the results show that the proposed RMFIsM method can mine recent maximal frequent itemsets efficiently

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

2012 ◽  
Vol 11 (1) ◽  
pp. 561-565
Author(s):  
Yimin Mao ◽  
Zhigang Chen ◽  
Lumin Yang ◽  
Junfeng Man ◽  
Lixin Liu
Keyword(s):  
Intrusion Detection ◽  
Data Streams ◽  
Frequent Itemsets ◽  
Detection Model ◽  
Model Based ◽  
Maximal Frequent Itemsets

Mining maximal frequent itemsets from data streams

2007 ◽  
Vol 33 (3) ◽  
pp. 251-262 ◽  
Author(s):  
Guojun Mao ◽  
Xindong Wu ◽  
Xingquan Zhu ◽  
Gong Chen ◽  
Chunnian Liu
Keyword(s):  
Data Streams ◽  
Frequent Itemsets ◽  
Maximal Frequent Itemsets
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