An algorithm for reducing the dimension and size of a sample for data exploration procedures

Kulczycki, Piotr; Łukasik, Szymon

doi:10.2478/amcs-2014-0011

Cited by 19 publications

(5 citation statements)

References 47 publications

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“…It is a case for traditional Principal Component Analysis algorithm. Here, we consider similar strategy assuring preserving distances between data sample elements and the metaheuristics of a Parallel Fast Simulated Annealing [16,17,22]. This approach is applicable to all EDA procedures.…”

Section: Optimization Inspired By Nature As a Tool For Eda -A Survey mentioning

confidence: 99%

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Kowalski¹,

Lłukasik²,

Kulczycki³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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This study contains a brief presentation of the basic tasks for Exploratory Data Analysis (EDA), namely: classification, clustering, reduction of data dimensionality and number of data instances as well as detection of outliers. Herein, solutions to the aforementioned problems incorporating a wide range of computational intelligence algorithms, in particular procedures based on collective intelligence, are under consideration. Furthermore, the combination of metaheuristic algorithms with basic EDA procedures applied and verified within many domains of science, technology and engineering are being presented.

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Section: Optimization Inspired By Nature As a Tool For Eda -A Survey mentioning

confidence: 99%

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Kowalski¹,

Lłukasik²,

Kulczycki³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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“…A detailed description of the methodology presented here can be found in the work [40] as well as in the paper [41] which will appear soon.…”

Section: Summary and Finals Remarksmentioning

confidence: 99%

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

Kulczycki

Łukasik

2014

Studies in Computational Intelligence

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Abstract.A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original version of the heuristic Parallel Fast Simulated Annealing method was used. In addition, those elements which change the location significantly as a result of the transformation, may be eliminated or assigned smaller weights for further analysis. As well as reducing the sample size, this also improves the quality of the applied methodology of knowledge extraction. Experimental research confirmed the usefulness of the procedure worked out in a broad range of problems of exploratory data analysis such as clustering, classification, identification of outliers and others.

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“…The latter arises mostly from a number of phenomena occurring in data sets of this type, known in literature as "the curse of multidimensionality". Above all, this includes the exponential growth in sample size, necessary to achieve appropriate effectiveness of data analysis methods with increasing dimension, as well as the vanishing difference between near and far points (norm concentration) using standard distance metrics [30].…”

Section: Data Volume Problem In Observational Astronomymentioning

confidence: 99%

Survey of Object-Based Data Reduction Techniques in Observational Astronomy

et al. 2016

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Abstract:Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.

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An algorithm for reducing the dimension and size of a sample for data exploration procedures

Cited by 19 publications

References 47 publications

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Methods of Collective Intelligence in Exploratory Data Analysis: A Research Survey

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

Survey of Object-Based Data Reduction Techniques in Observational Astronomy

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