Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints

Antunes, Cláudia; Oliveira, Arlindo L.

doi:10.1007/3-540-45065-3_21

Cited by 41 publications

(41 citation statements)

References 5 publications

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“…Given two sequences α=<a 1 a 2 …a n > and β=<b 1 b 2 …b m > where α is called a subsequence of β, denoted as α⊆ β, if there exist integers 1≤j 1 <j 2 <…<j n ≤m such that a 1 ⊆b j1 , a 2 ⊆b j2 ,…,a n ⊆b jn. Here if α and β have the following sequences α=<(xy), t> and β=< (xyz), (zt)>, β is denoted as a super sequence of α [2,6]. In addition to the discovery of recurrent itemsets, sequential pattern mining requires the arrangement of those itemsets in a sequence.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Let s represents minimum support threshold for mining the database and let m =|C|. Then aim of mining frequently occurring itemset is to discover recurrent itemsets among | I s | different possible itemsets as represented in equation (i) below [6]:…”

Section: Literature Reviewmentioning

confidence: 99%

“…Now suppose that the database has sequences with at most p itemsets and each itemset has at most single item such that there are m p possible different sequences the different variable length sequences are given by equation (ii) as below [6]:…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Various Sequential Pattern Mining Algorithms

Grover¹

2014

IJCA

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In Sequential pattern mining represents an important class of data mining problems with wide range of applications. It is one of the very challenging problems because it deals with the careful scanning of a combinatorially large number of possible subsequence patterns. Broadly sequential pattern ming algorithms can be classified into three types namely Apriori based approaches, Pattern growth algorithms and Early pruning algorithms. These algorithms have further classification and extensions. Detailed explanation of each algorithm along with its important features, pseudo code, advantages and disadvantages is given in the subsequent sections of the paper. At the end a comparative analysis of all the algorithms with their supporting features is given in the form of a table. This paper tries to enrich the knowledge and understanding of various approaches of sequential pattern mining.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Various Sequential Pattern Mining Algorithms

Grover¹

2014

IJCA

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“…In [7], an algorithm was developed for a text collection, which is different from finding all the MFS into a single text. The algorithms for getting all MFS can be classified as Apriori-based (typical) and Pattern-growth methods [8].…”

Section: Related Workmentioning

confidence: 99%

A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text

García-Hernández

Martínez-Trinidad

Carrasco-Ochoa

2004

Lecture Notes in Computer Science

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Abstract. One of the sequential pattern mining problems is to find the maximal frequent sequences in a database with a β support. In this paper, we propose a new algorithm to find all the maximal frequent sequences in a text instead of a database. Our algorithm in comparison with the typical sequential pattern mining algorithms avoids the joining, pruning and text scanning steps. Some experiments have shown that it is possible to get all the maximal frequent sequences in a few seconds for medium texts.

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“…Application of this approach to the treatment of sequential data results in one important special case. The process of finding all sub-sequences that occur often on a specified sequence database and have minimum support threshold is known as sequential pattern mining [1]. Data is normally assumed to be centralized, memory-resident, and static by conventional methods for sequential mining.…”

Section: Introductionmentioning

confidence: 99%

Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database

Mallick

Garg

Grover

2013

IJCIS

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Sequential pattern mining is a vital data mining task to discover the frequently occurring patterns in sequence databases. As databases develop, the problem of maintaining sequential patterns over an extensively long period of time turn into essential, since a large number of new records may be added to a database. To reflect the current state of the database where previous sequential patterns would become irrelevant and new sequential patterns might appear, there is a need for efficient algorithms to update, maintain and manage the information discovered. Several efficient algorithms for maintaining sequential patterns have been developed. Here, we have presented an efficient algorithm to handle the maintenance problem of CFM-sequential patterns (Compact, Frequent, Monetaryconstraints based sequential patterns). In order to efficiently capture the dynamic nature of data addition and deletion into the mining problem, initially, we construct the updated CFM-tree using the CFM patterns obtained from the static database. Then, the database gets updated from the distributed sources that have data which may be static, inserted, or deleted. Whenever the database is updated from the multiple sources, CFM tree is also updated by including the updated sequence. Then, the updated CFM-tree is used to mine the progressive CFM-patterns using the proposed tree pattern mining algorithm. Finally, the experimentation is carried out using the synthetic and real life distributed databases that are given to the progressive CFM-miner. The experimental results and analysis provides better results in terms of the generated number of sequential patterns, execution time and the memory usage over the existing IncSpan algorithm.

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Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints

Cited by 41 publications

References 5 publications

Comparative Study of Various Sequential Pattern Mining Algorithms

Comparative Study of Various Sequential Pattern Mining Algorithms

A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text

Progressive CFM-Miner: An Algorithm to Mine CFM – Sequential Patterns from a Progressive Database

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