TESTING FOR REVEALING OF DATA STRUCTURE BASED ON THE HYBRID APPROACH
Volodymyr Mosorov
w.mosorow@kis.p.lodz.plLodz University of Technology, Institute of Applied Computer Science (Poland)
Taras Panskyi
Lodz University of Technology, Institute of Applied Computer Science (Poland)
Sebastian Biedron
Lodz University of Technology, Institute of Applied Computer Science (Poland)
Abstract
In this paper testing for revealing data structure based on a hybrid approach has been presented. The hybrid approach used during the testing suggests defining a pre-clustering hypothesis, defining a pre-clustering statistic and assuming the homogeneity of the data under pre-defined hypothesis, applying the same clustering procedure for a data set of interest, and comparing results obtained under the pre-clustering statistic with the results from the data set of interest. The pros and cons of the hybrid approach have been also considered.
Keywords:
pre-clustering hypothesis, data group structure testing, group structure revealingReferences
Mosorov V., Tomczak L.: Image texture defect detection method using fuzzy c-means clustering for visual inspection systems. Arabian Journal for Science and Engineering 39(4)/2014, 3013–3022 [DOI:10.1007/s13369-013-0920-7].
Google Scholar
Kumar D., Bezdek J.C., Rajasegarar S., Leckie C., Palaniswami M.: A visual-numeric approach to clustering and anomaly detection for trajectory data. The Visual Computer, December 2015 [DOI:10.1007/s00371-015-1192-x].
Google Scholar
Zhang S., Hu W., Wang T., Liu J., Zhang Y.: Speaker Clustering Aided by Visual Dialogue Analysis. Advances in Multimedia Information Processing – PCM 2008. Springer Science + Business Media. 693–702.
Google Scholar
Strauss D.J., Riverside C.: A model for clustering. Biometrika 62(2)/ 1975, 467–475 [DOI:10.1093/biomet/62.2.467].
Google Scholar
Bock H.H.: On some significance tests in cluster analysis. Journal of Classification 2(1)/1985, 77–108 [DOI:10.1007/bf01908065].
Google Scholar
Hartigan J.A., Mohanty S.: The runt test for multimodality. Journal of Classification 9(1)/1992, 63–70 [DOI:10.1007/bf02618468].
Google Scholar
Hennig C., Lin C-J.: Flexible parametric bootstrap for testing homogeneity against clustering and assessing the number of clusters. Statistics and Computing 25(4)/2015, 821–833 [DOI:10.1007/s11222-015-9566-5].
Google Scholar
Hautaniemi S., Edgren H., Vesanen P. et al.: A novel strategy for microarray quality control using Bayesian networks. Bioinformatics 19(16)/2003, 2031–2038 [DOI:10.1093/bioinformatics/btg275].
Google Scholar
Everitt B.S., Landau S., Leese M., Stahl D.: Cluster analysis. John Wiley & Sons, January 7, 2011.
Google Scholar
Gordon A.: Studies in Classification, Data Analysis, and Knowledge Organization. Gordon AD. From Data to Knowledge. Springer Science + Business Media 1996, 32–44.
Google Scholar
Fisher R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2)/1936, 179–188 [DOI:10.1111/j.1469-1809.1936.tb02137.x].
Google Scholar
Gorman R.P., Sejnowski T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1/1988, 75–89.
Google Scholar
Ripley B.D.: Neural networks and related methods for classification. Journal of the Royal Statistical Society - Series B (Methodological) 56(3)/1994, 409–456 [DOI:10.2307/2346118].
Google Scholar
Authors
Volodymyr Mosorovw.mosorow@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland
Authors
Taras PanskyiLodz University of Technology, Institute of Applied Computer Science Poland
Authors
Sebastian BiedronLodz University of Technology, Institute of Applied Computer Science Poland
Statistics
Abstract views: 261PDF downloads: 60
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Monika Zbrojewska, Volodymyr Mosorov, Sebastian Biedron, Taras Panskyi, HOW DO WE DEFINE CYBERCRIME? , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 6 No. 2 (2016)
- Taras Panskyi, Volodymyr Mosorov, A STEP TOWARDS THE MAJORITY-BASED CLUSTERING VALIDATION DECISION FUSION METHOD , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 11 No. 2 (2021)
- Fatma Mbarek, Volodymyr Mosorov, Rafał Wojciechowski, WEB SERVER LATENCY REDUCTION STUDY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 2 (2017)
- Volodymyr Mosorov, Krzysztof Grudzień, Dominik Sankowski, FLOW VELOCITY MEASUREMENT METHODS USING ELECTRICAL CAPACITANCE TOMOGRAPHY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 1 (2017)
- Volodymyr Mosorov, Sebastian Biedron, Taras Panskyi, THE DEPENDENCE BETWEEN THE NUMBER OF ROUNDS AND IMPLEMENTED NODES IN LEACH ROUTING PROTOCOL-BASED SENSOR NETWORKS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 3 (2017)
- Volodymyr Mosorov, Taras Panskyi, Sebastian Biedron, ALTERNATIVE TERMINATION CRITERION FOR K-SPECIFIED CRISP DATA CLUSTERING ALGORITHMS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 7 No. 3 (2017)
- Ayoub Saoud, Volodymyr Mosorov, Krzysztof Grudzień, SWIRL FLOW ANALYSIS BASED ON ELECTRICAL CAPACITANCE TOMOGRAPHY , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 6 No. 2 (2016)
- Volodymyr Mosorov, Taras Panskyi, Sebastian Biedron, MODIFIED, COMPLEMENTED TAXONOMY OF FAULTS IN FAULT-TOLERANT REAL-TIME SYSTEMS , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 2 (2018)
- Volodymyr Mosorov, Taras Panskyi, Sebastian Biedron, MODIFIED ALTERNATIVE DECISION RULE IN THE PRE-CLUSTERING ALGORITHM , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 6 No. 2 (2016)
- Volodymyr Mosorov, Sebastian Biedron, Taras Panskyi, THE APPLICATION OF REDUNDANCY IN LEACH PROTOCOL , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 8 No. 2 (2018)
