Novel approach to data discretization

Borowik, Grzegorz; Kowalski, Karol J.; Jankowski, C.

doi:10.1117/12.2205916

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the large computational complexity for large data sets, statistical algorithms are useful. Examples of such approaches are the algorithm of feature extraction described by Korzen and Jaroszewicz (2005) as well as the algorithm of data discretization described by Borowik et al (2015a).…”

mentioning

confidence: 99%

Optimization on the Complementation Procedure Towards Efficient Implementation of the Index Generation Function

Borowik

2018

International Journal of Applied Mathematics and Computer Science

Self Cite

View full text Add to dashboard Cite

In the era of big data, solutions are desired that would be capable of efficient data reduction. This paper presents a summary of research on an algorithm for complementation of a Boolean function which is fundamental for logic synthesis and data mining. Successively, the existing problems and their proposed solutions are examined, including the analysis of current implementations of the algorithm. Then, methods to speed up the computation process and efficient parallel implementation of the algorithm are shown; they include optimization of data representation, recursive decomposition, merging, and removal of redundant data. Besides the discussion of computational complexity, the paper compares the processing times of the proposed solution with those for the well-known analysis and data mining systems. Although the presented idea is focused on searching for all possible solutions, it can be restricted to finding just those of the smallest size. Both approaches are of great application potential, including proving mathematical theorems, logic synthesis, especially index generation functions, or data processing and mining such as feature selection, data discretization, rule generation, etc. The problem considered is NP-hard, and it is easy to point to examples that are not solvable within the expected amount of time. However, the solution allows the barrier of computations to be moved one step further. For example, the unique algorithm can calculate, as the only one at the moment, all minimal sets of features for few standard benchmarks. Unlike many existing methods, the algorithm additionally works with undetermined values. The result of this research is an easily extendable experimental software that is the fastest among the tested solutions and the data mining systems.

show abstract

mentioning

confidence: 99%