### Abstract

Sequential pattern mining is an important data mining method with broad applications that can extract frequent sequences while maintaining their order. However, it is important to identify item intervals of sequential patterns extracted by sequential pattern mining. For example, a sequence < A;B > with a 1-day interval and a sequence < A;B > with a 1-year interval are completely different; the former sequence may have some association, while the latter may not. To adopt item intervals, two approaches have been proposed for integration of item intervals with sequential pattern mining; (1) constraint-based mining and (2) extended sequence-based mining. However, although constraint-based mining approach avoids the extraction of sequences with non-interest time intervals such as too long intervals it has setbacks in that it is difficult to specify optimal constraints related to item interval, and users must re-execute constraint-based algorithms with changing constraint values. On the other hand, extended sequence-based mining approach does not need to specify constraints and re-execute. Since extended sequence-based mining approach cannot adopt any constraints based on time intervals, it may extract meaningless patterns, such as sequences with too long item intervals. This means these two approaches have not only advantages but also disadvantages. To solve this problem, in this paper, we generalize sequential pattern mining with item interval. The generalization includes three points; (a) a capability to handle two kinds of item interval measurement, item gap and time interval, (b) a capability to handle extended sequences which are defined by inserting pseudo items based on the interval itemization function, and (c) adopting four item interval constraints. Generalized sequential pattern mining is able to substitute all types of conventional sequential pattern mining algorithms with item intervals. Using Japanese earthquake data, we have confirmed that our proposed algorithm is able to extract sequential patterns with item interval, defined in a flexible manner by the interval itemization function.

Original language | English |
---|---|

Pages (from-to) | 51-60 |

Number of pages | 10 |

Journal | Journal of Computers |

Volume | 1 |

Issue number | 3 |

Publication status | Published - 2006 |

### Fingerprint

### Keywords

- Data Mining
- Gap
- Item Intervals
- Sequential Pattern Mining
- Time-stamp

### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*Journal of Computers*,

*1*(3), 51-60.

**Generalized sequential pattern mining with item intervals.** / Hirate, Yu; Yamana, Hayato.

Research output: Contribution to journal › Article

*Journal of Computers*, vol. 1, no. 3, pp. 51-60.

}

TY - JOUR

T1 - Generalized sequential pattern mining with item intervals

AU - Hirate, Yu

AU - Yamana, Hayato

PY - 2006

Y1 - 2006

N2 - Sequential pattern mining is an important data mining method with broad applications that can extract frequent sequences while maintaining their order. However, it is important to identify item intervals of sequential patterns extracted by sequential pattern mining. For example, a sequence < A;B > with a 1-day interval and a sequence < A;B > with a 1-year interval are completely different; the former sequence may have some association, while the latter may not. To adopt item intervals, two approaches have been proposed for integration of item intervals with sequential pattern mining; (1) constraint-based mining and (2) extended sequence-based mining. However, although constraint-based mining approach avoids the extraction of sequences with non-interest time intervals such as too long intervals it has setbacks in that it is difficult to specify optimal constraints related to item interval, and users must re-execute constraint-based algorithms with changing constraint values. On the other hand, extended sequence-based mining approach does not need to specify constraints and re-execute. Since extended sequence-based mining approach cannot adopt any constraints based on time intervals, it may extract meaningless patterns, such as sequences with too long item intervals. This means these two approaches have not only advantages but also disadvantages. To solve this problem, in this paper, we generalize sequential pattern mining with item interval. The generalization includes three points; (a) a capability to handle two kinds of item interval measurement, item gap and time interval, (b) a capability to handle extended sequences which are defined by inserting pseudo items based on the interval itemization function, and (c) adopting four item interval constraints. Generalized sequential pattern mining is able to substitute all types of conventional sequential pattern mining algorithms with item intervals. Using Japanese earthquake data, we have confirmed that our proposed algorithm is able to extract sequential patterns with item interval, defined in a flexible manner by the interval itemization function.

AB - Sequential pattern mining is an important data mining method with broad applications that can extract frequent sequences while maintaining their order. However, it is important to identify item intervals of sequential patterns extracted by sequential pattern mining. For example, a sequence < A;B > with a 1-day interval and a sequence < A;B > with a 1-year interval are completely different; the former sequence may have some association, while the latter may not. To adopt item intervals, two approaches have been proposed for integration of item intervals with sequential pattern mining; (1) constraint-based mining and (2) extended sequence-based mining. However, although constraint-based mining approach avoids the extraction of sequences with non-interest time intervals such as too long intervals it has setbacks in that it is difficult to specify optimal constraints related to item interval, and users must re-execute constraint-based algorithms with changing constraint values. On the other hand, extended sequence-based mining approach does not need to specify constraints and re-execute. Since extended sequence-based mining approach cannot adopt any constraints based on time intervals, it may extract meaningless patterns, such as sequences with too long item intervals. This means these two approaches have not only advantages but also disadvantages. To solve this problem, in this paper, we generalize sequential pattern mining with item interval. The generalization includes three points; (a) a capability to handle two kinds of item interval measurement, item gap and time interval, (b) a capability to handle extended sequences which are defined by inserting pseudo items based on the interval itemization function, and (c) adopting four item interval constraints. Generalized sequential pattern mining is able to substitute all types of conventional sequential pattern mining algorithms with item intervals. Using Japanese earthquake data, we have confirmed that our proposed algorithm is able to extract sequential patterns with item interval, defined in a flexible manner by the interval itemization function.

KW - Data Mining

KW - Gap

KW - Item Intervals

KW - Sequential Pattern Mining

KW - Time-stamp

UR - http://www.scopus.com/inward/record.url?scp=57049152226&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=57049152226&partnerID=8YFLogxK

M3 - Article

VL - 1

SP - 51

EP - 60

JO - Journal of Computers

JF - Journal of Computers

SN - 1796-203X

IS - 3

ER -