A Weighted Closed Sequential Patterns Mining Algorithm Based on Regular Expression Constraints

2012 ◽  
Vol 4 (1) ◽  
pp. 415-422
Author(s):  
Wuzhou Dong ◽  
Mingyue Li ◽  
Guoyan Huang ◽  
Jiadong Ren ◽  
Lijuan Chen
Keyword(s):  
Regular Expression ◽  
Sequential Patterns ◽  
Mining Algorithm

Mining sequential patterns with regular expression constraints

2002 ◽  
Vol 14 (3) ◽  
pp. 530-552 ◽  
Author(s):  
M. Garofalakis ◽  
R. Rastogi ◽  
K. Shim
Keyword(s):  
Regular Expression ◽  
Sequential Patterns

Campus Data Analysis Based on Positive and Negative Sequential Patterns

2019 ◽  
Vol 33 (05) ◽  
pp. 1959016 ◽  
Author(s):  
Xiaoqi Jiang ◽  
Tiantian Xu ◽  
Xiangjun Dong
Keyword(s):  
College Students ◽  
Time Series ◽  
Data Analysis ◽  
Sequential Patterns ◽  
Negative Events ◽  
Positive Events ◽  
Mining Algorithm ◽  
Consumption Data ◽  
Academic Grades ◽  
The Relationship

Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.

The Sequential Pattern Mining Algorithm MHSP Based on MH

2011 ◽  
Vol 63-64 ◽  
pp. 425-430
Author(s):  
Jun Wang ◽  
Ya Qiong Jiang
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Important Method ◽  
The Real ◽  
Mining Algorithm ◽  
Large Projection ◽  
Growth Approach

Pattern growth approach is an important method in sequential pattern mining. Projection database based on the method is introduced in PrefixSpan, and the PrefixSpan algorithm can solve the problem of mining sequential patterns. But relative to large projection database, the performance of PrefixSpan is affected. Inspired by the prefix-divide method and MH structure, this paper proposed a new algorithm MHSP for sequential pattern mining. Based on the real datasets, experimental results show that the performance of MHSP algorithm is more than twice as fast as PrefixSpan.

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.

A Fuzzy Data Mining Algorithm for Finding Sequential Patterns

2003 ◽  
Vol 11 (02) ◽  
pp. 173-193 ◽  
Author(s):  
Yi-Chung Hu ◽  
Ruey-Shun Chen ◽  
Gwo-Hshiung Tzeng ◽  
Jia-Hourng Shieh
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Effective Algorithm ◽  
Mining Algorithm ◽  
Fuzzy Knowledge

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

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