2002
DOI: 10.1016/s0306-4379(02)00008-x
|View full text |Cite
|
Sign up to set email alerts
|

Mining hybrid sequential patterns and sequential rules

Abstract: The problem addressed in this paper is to discover the frequently occurred sequential patterns from databases. Basically, the existing studies on finding sequential patterns can be roughly classified into two main categories. In the first category, the discovered patterns are continuous patterns, where all the elements in the pattern appear in consecutive positions in transactions. The second category is to mine discontinuous patterns, where the adjacent elements in the pattern need not appear consecutively in… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
17
0

Year Published

2005
2005
2020
2020

Publication Types

Select...
7
3

Relationship

2
8

Authors

Journals

citations
Cited by 37 publications
(17 citation statements)
references
References 27 publications
0
17
0
Order By: Relevance
“…Here, our approach uses a continuous sequence [23], which satisfies a condition that all elements in the sequence must appear consecutively. Discontinuous one is not adopted.…”
Section: Sequential Characteristics Of Categoriesmentioning
confidence: 99%
“…Here, our approach uses a continuous sequence [23], which satisfies a condition that all elements in the sequence must appear consecutively. Discontinuous one is not adopted.…”
Section: Sequential Characteristics Of Categoriesmentioning
confidence: 99%
“…Ruotsalainen et al [41] design the Gais genetic algorithm to detect interleaved patterns in an unsupervised learning fashion. Other approaches have been proposed to mine discontinuous patterns [7],[34],[50], in different types of sequence datasets and to allow variations in occurrences of the patterns [38]. Aspects of these earlier techniques are useful in analyzing sensor sequence data.…”
Section: Related Workmentioning
confidence: 99%
“…Since its introduction [2], sequential pattern mining has drawn a great deal of attention from academic researchers as well as practitioners. As a result of its success, extensions have been proposed in various directions, including: (1) other variants of sequential patterns, including maximum patterns [2], similar patterns [1,20,31], cyclic patterns [14,15,23], continuous patterns [34], traversal patterns [10,27], multidimensional patterns [35], hybrid patterns [5], and nonambiguous temporal patterns [33], (2) improved methods for mining sequential Single-time-interval Sequential Patterns:…”
Section: Related Workmentioning
confidence: 99%