An implementation of logical analysis of data

Boros, Endre; Hammer, Peter L.; Ibaraki, Toshihide; Kogan, Alexander; Mayoraz, Eddy; Muchnik, Ilya

doi:10.1109/69.842268

Cited by 331 publications

(301 citation statements)

References 15 publications

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“…The largest frequent lists generated by the algorithm are I 1 , I 2, I 3 ,, I 1 , I 3 , I 4 After giving the data to Apriori algorithm it also produce the same results. With the same largest frequent sets contain, I 1 , I 2, I 3 I 1 , I 3 , I 4 After loading more data, the total records in the database became 500.…”

Section: Figure 4apriori Algorithm Resultssupporting

confidence: 49%

“…With the same largest frequent sets contain, I 1 , I 2, I 3 I 1 , I 3 , I 4 After loading more data, the total records in the database became 500. Applying Apriori and SI algorithms on the updated database, the results produced are given.…”

Section: Figure 4apriori Algorithm Resultsmentioning

confidence: 99%

“…In this [7] transformation we map each observation u = (uA, uB,…) of the given numerical data set to a binary vector x(u) = (x1, x2,…) Є {0, 1}n by defining e.g. x1 = 1 iif uA ≥ α1, x2 = 1 iif uB ≥ α2, etc, and in such a way that if u and v represent, respectively, a positive and negative observation point, then x(u) ≠ x(v).…”

Section: Binarizationmentioning

confidence: 99%

“…The logical analysis of data was originally developed for the analysis of datasets whose attributes take only binary (0-1) values [4,5,8].Since it turned out later that most of the real-life applications include attributes taking real values, a "binarization" method was proposed in [3]. The purpose of binarization is the transformation of a database of any type into a "Boolean database".…”

Section: Logical Data Analysismentioning

confidence: 99%

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Computer Science Education

2013

Encyclopedia of Sciences and Religions

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Computer science or computing science (sometimes abbreviated CS) is the learning of the theoretical foundations of information and computation, and of practical techniques for their execution and application in computer systems. It is often described as the efficient study of algorithmic processes that produce, explain, and transform information. In this work, I use Data Mining algorithms from the field of computer science for the analysis process to prove experimentally and practically that how reliable, efficient and fast are these for the analysis of data in education? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained results will be tested by applying the approach to the databases and data warehouses of different sizes with different threshold values. The results produce will be of different strength from short to the largest sets of data list. By this, we may get the results for different purposes e.g. making future education plan.

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Section: Figure 4apriori Algorithm Resultssupporting

confidence: 49%

Section: Figure 4apriori Algorithm Resultsmentioning

confidence: 99%

Section: Binarizationmentioning

confidence: 99%

Section: Logical Data Analysismentioning

confidence: 99%

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Computer Science Education

2013

Encyclopedia of Sciences and Religions

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“…In their column generation subproblem, the objective is precisely that of maximum agreement; if the space of possible weak learners consists of all monomials, the subproblem is precisely to maximize (2). Monomial hypotheses (also called logical patterns) are also a basic building block in the logical analysis of data (LAD) methodology [4], where linear programming techniques are also used to compute the discriminant function.…”

Section: Problem Statement and Introductionmentioning

confidence: 99%

An Improved Branch-and-Bound Method for Maximum Monomial Agreement

Eckstein

Goldberg

2012

INFORMS Journal on Computing

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The N P-hard maximum monomial agreement (MMA) problem consists of finding a single logical conjunction that best fits a weighted dataset of "positive" and "negative" binary vectors. Computing classifiers using boosting methods involves a maximum agreement subproblem at each iteration, although such subproblems are typically solved by heuristic methods. Here, we describe an exact branch and bound method for maximum agreement over Boolean monomials, improving on the earlier work of Goldberg and Shan [14]. In particular, we develop a tighter upper bounding function and an improved branching procedure that exploits knowledge of the bound and the dataset, while having a lower branching factor. Experimental results show that the new method is able to solve larger problem instances and runs faster within a linear programming boosting procedure applied to medium-sized datasets from the UCI repository.

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Large Margin Rule‐Based Classifiers

Bonates

2011

Wiley Encyclopedia of Operations Research and Management Science

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We introduce an optimization approach for the construction of large margin rule‐based classifiers. We base our description on the Logical Analysis of Data (LAD) methodology, but the same approach can be applied to different rule‐based classification algorithms. The novelty in our algorithm relies on the fact that it unifies the distinct tasks of pattern (rule) generation and creation of a so‐called discriminant function for classification. Moreover, the algorithm has a single parameter, thus, significantly reducing the necessity for parameter calibration when learning a new data set. We investigate how accurate the LAD classification models built with our algorithm are and how they compare to other machine learning models.

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An implementation of logical analysis of data

Cited by 331 publications

References 15 publications

Computer Science Education

Computer Science Education

An Improved Branch-and-Bound Method for Maximum Monomial Agreement

Large Margin Rule‐Based Classifiers

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