Association Rules and Sequential Patterns

2011 ◽  
pp. 17-62 ◽  
Author(s):  
Bing Liu
Keyword(s):  
Association Rules ◽  
Sequential Patterns

e-NSPFI: Efficient Mining Negative Sequential Pattern from Both Frequent and Infrequent Positive Sequential Patterns

2017 ◽  
Vol 31 (02) ◽  
pp. 1750002 ◽  
Author(s):  
Yongshun Gong ◽  
Tiantian Xu ◽  
Xiangjun Dong ◽  
Guohua Lv
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
Optimization Method ◽  
Negative Association ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Negative Association Rules ◽  
Storage Optimization

Negative sequential patterns (NSPs), which focus on nonoccurring but interesting behaviors (e.g. missing consumption records), provide a special perspective of analyzing sequential patterns. So far, very few methods have been proposed to solve for NSP mining problem, and these methods only mine NSP from positive sequential patterns (PSPs). However, as many useful negative association rules are mined from infrequent itemsets, many meaningful NSPs can also be found from infrequent positive sequences (IPSs). The challenge of mining NSP from IPS is how to constrain which IPS could be available used during NSP process because, if without constraints, the number of IPS would be too large to be handled. So in this study, we first propose a strategy to constrain which IPS could be available and utilized for mining NSP. Then we give a storage optimization method to hold this IPS information. Finally, an efficient algorithm called Efficient mining Negative Sequential Pattern from both Frequent and Infrequent positive sequential patterns (e-NSPFI) is proposed for mining NSP. The experimental results show that e-NSPFI can efficiently find much more interesting negative patterns than e-NSP.

COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY

2007 ◽  
Vol 06 (01) ◽  
pp. 15-41 ◽  
Author(s):  
XIANGJI HUANG
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Interestingness Measures ◽  
Web Based ◽  
Log Data ◽  
Web Usage ◽  
Different Types ◽  
Mining Association Rules ◽  
Types Of Information

A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection of Livelink log data. Livelink is a web-based product of Open Text Corporation, which provides automatic management and retrieval of different types of information objects over an intranet, an extranet or the Internet. We report our experience in preprocessing raw log data, mining association rules and sequential patterns from the log data, and identifying interesting rules and patterns by use of interestingness measures and some pruning methods. In particular, we evaluate a number of interestingness measures in terms of their effectiveness in finding interesting association rules and sequential patterns. Our results show that some measures are much more effective than others.

scholarly journals Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1875-1885
Author(s):  
Ping Qiu ◽  
Long Zhao ◽  
Weiyang Chen ◽  
Tiantian Xu ◽  
Xiangjun Dong
Keyword(s):  
Association Rules ◽  
Negative Association ◽  
Sequential Patterns ◽  
Minimum Support ◽  
Negative Association Rules ◽  
Multiple Minimum Supports

Negative sequential patterns (NSP) referring to both occurring items (positive items) and nonoccurring items (negative items) play a very important role in many real applications. Very few methods have been proposed to mine NSP and most of them only mine NSP from frequent positive sequences, not from infrequent positive sequences (IPS). In fact, many useful NSP can be mined from IPS, just like many useful negative association rules can be obtained from infrequent itemsets. e-NSPFI is a method to mine NSP from IPS, but its constraint is very strict to IPS and many useful NSP would be missed. In addition, e-NSPFI only uses a single minimum support, which implicitly assumes that all items in the database are of the similar frequencies. In order to solve the above problems and optimize NSP mining, a 2-level multiple minimum supports (2-LMMS) constraint to IPS is proposed in this paper. Firstly, we design two minimum supports constraints to mine frequent and infrequent positive sequences. Secondly, we use Select Actionable Pattern (SAP) method to select actionable NSP. Finally, we propose a corresponding algorithm msNSPFI to mine actionable NSP from IPS with 2-LMMS. Experiment results show that msNSPFI is very efficient for mining actionable NSP.

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