Treatment of missing values for association rules

Ragel, Arnaud; Crémilleux, Bruno

doi:10.1007/3-540-64383-4_22

Cited by 48 publications

(41 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Formula (2) shows the definition. It is based on an idea similar to the support for missing values in tabular structured data [10,11]. The criterion is called a characteristic support hereafter…”

Section: 2mentioning

confidence: 99%

See 1 more Smart Citation

Discovery of Characteristic Patterns from Transactions with Their Classes

Sakurai

2012

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This paper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes assigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method that efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by using their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data registered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel shops.

show abstract

Section: 2mentioning

confidence: 99%

“…In the case of the data, missing values are often included. In order to deal with the values, [10][11][12] propose new evaluation criteria of the patterns.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Characteristic Patterns from Transactions with Their Classes

Sakurai

2012

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

show abstract

“…We have proposed in [14] the RAR algorithm to correct the weakness of usual association rules algorithms [2] in mining databases with multiple missing values. The efficiency of this algorithm to extract quickly all the associations contained in such a database, allows to use it for the missing values problem.…”

Section: Introductionmentioning

confidence: 99%

“…In Sect. 3 we present the RAR algorithm [14] and show in Sect. 4 its interest for the missing values problem in MVC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preprocessing of missing values using robust association rules

Ragel

1998

Principles of Data Mining and Knowledge Discovery

Self Cite

View full text Add to dashboard Cite

Abstract. Many of analysis tasks have to deal with missing values and have developed specific and internal treatments to guess them. In this paper we present an external method for this problem to improve performances of completion and especially declarativity and interactions with the user. Such qualities will allow to use it for the data cleaning step of the KDD x process [6]. The core of this method, called MVC (Missing Values Completion), is the RAR 2 algorithm that we have proposed in [14]. This algorithm extends the concept of association rules[l] for databases with multiple missing values. It allows MVC to be an efficient preprocessing method: in our experiments with the c4.5[12] decision tree program, MVC has permitted to divide, up to two, the error rate in classification, independently of a significant gain of declarativity.

show abstract

Data Farming: Concepts and Methods

Kusiak

Massive Computing

View full text Add to dashboard Cite

Treatment of missing values for association rules

Cited by 48 publications

References 4 publications

Discovery of Characteristic Patterns from Transactions with Their Classes

Discovery of Characteristic Patterns from Transactions with Their Classes

Preprocessing of missing values using robust association rules

Data Farming: Concepts and Methods

Contact Info

Product

Resources

About