### Abstract

Formal concept analysis and probability logic are two useful tools for data analysis. Data is usually represented as a two-dimensional context of objects and features. FCA discovers dependencies within the data based on the relation among objects and features. On the other hand, the probability logic represents and reasons with both statistical and propositional probability among data. We propose SPICE - Symbolic integration of Probability Inference and Concept Extraction, which provides a more flexible and robust framework for data mining tasks. Within SPICE, we formalize the important notions of data mining, such as concepts and patterns, and develop new notions such as maximal potentially useful patterns. In this paper, we formalize the association rule mining in SPICE and propose an enhanced rule mining approach, called SPICE association rule mining, to solve the problem of time inefficiency and rule redundancy in general association rule mining. We show an application of the SPICE approach in the Geo-spatial Decision Support System (GDSS). The experimental results show that SPICE can efficiently and effectively discover a succinct set of interesting association rules.

Original language | English (US) |
---|---|

Pages (from-to) | 467-485 |

Number of pages | 19 |

Journal | Fundamenta Informaticae |

Volume | 78 |

Issue number | 4 |

State | Published - Sep 18 2007 |

### Fingerprint

### Keywords

- Association rules
- Data mining
- FCA
- Important items
- Probability logic
- Redundant rules

### ASJC Scopus subject areas

- Theoretical Computer Science
- Algebra and Number Theory
- Information Systems
- Computational Theory and Mathematics

### Cite this

*Fundamenta Informaticae*,

*78*(4), 467-485.

**SPICE : A new framework for data mining based on probability logic and formal concept analysis.** / Jiang, Liying; Deogun, Jitender.

Research output: Contribution to journal › Article

*Fundamenta Informaticae*, vol. 78, no. 4, pp. 467-485.

}

TY - JOUR

T1 - SPICE

T2 - A new framework for data mining based on probability logic and formal concept analysis

AU - Jiang, Liying

AU - Deogun, Jitender

PY - 2007/9/18

Y1 - 2007/9/18

N2 - Formal concept analysis and probability logic are two useful tools for data analysis. Data is usually represented as a two-dimensional context of objects and features. FCA discovers dependencies within the data based on the relation among objects and features. On the other hand, the probability logic represents and reasons with both statistical and propositional probability among data. We propose SPICE - Symbolic integration of Probability Inference and Concept Extraction, which provides a more flexible and robust framework for data mining tasks. Within SPICE, we formalize the important notions of data mining, such as concepts and patterns, and develop new notions such as maximal potentially useful patterns. In this paper, we formalize the association rule mining in SPICE and propose an enhanced rule mining approach, called SPICE association rule mining, to solve the problem of time inefficiency and rule redundancy in general association rule mining. We show an application of the SPICE approach in the Geo-spatial Decision Support System (GDSS). The experimental results show that SPICE can efficiently and effectively discover a succinct set of interesting association rules.

AB - Formal concept analysis and probability logic are two useful tools for data analysis. Data is usually represented as a two-dimensional context of objects and features. FCA discovers dependencies within the data based on the relation among objects and features. On the other hand, the probability logic represents and reasons with both statistical and propositional probability among data. We propose SPICE - Symbolic integration of Probability Inference and Concept Extraction, which provides a more flexible and robust framework for data mining tasks. Within SPICE, we formalize the important notions of data mining, such as concepts and patterns, and develop new notions such as maximal potentially useful patterns. In this paper, we formalize the association rule mining in SPICE and propose an enhanced rule mining approach, called SPICE association rule mining, to solve the problem of time inefficiency and rule redundancy in general association rule mining. We show an application of the SPICE approach in the Geo-spatial Decision Support System (GDSS). The experimental results show that SPICE can efficiently and effectively discover a succinct set of interesting association rules.

KW - Association rules

KW - Data mining

KW - FCA

KW - Important items

KW - Probability logic

KW - Redundant rules

UR - http://www.scopus.com/inward/record.url?scp=34548570766&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548570766&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:34548570766

VL - 78

SP - 467

EP - 485

JO - Fundamenta Informaticae

JF - Fundamenta Informaticae

SN - 0169-2968

IS - 4

ER -