Outside the box : an alternative data analytics framework

• Autorzy: Angelov P.

Identyfikatory

DOI

DOI 10.14313/JAMRIS_2-2014/16

• Języki publikacji: EN

Abstrakty

EN

In this paper, an alternative framework for data analytics is proposed which is based on the spatially-aware concepts of eccentricity and typicality which represent the density and proximity in the data space. This approach is statistical, but differs from the traditional probability theory which is frequentist in nature. It also differs from the belief and possibility-based approaches as well as from the deterministic first principles approaches, although it can be seen as deterministic in the sense that it provides exactly the same result for the same data. It also differs from the subjective expert-based approaches such as fuzzy sets. It can be used to detect anomalies, faults, form clusters, classes, predictive models, controllers. The main motivation for introducing the new typicality- and eccentricity-based data analytics (TEDA) is the fact that real processes which are of interest for data analytics, such as climate, economic and financial, electro-mechanical, biological, social and psychological etc., are often complex, uncertain and poorly known, but not purely random. Unlike, purely random processes, such as throwing dices, tossing coins, choosing coloured balls from bowls and other games, real life processes of interest do violate the main assumptions which the traditional probability theory requires. At the same time they are seldom deterministic (more precisely, have always uncertainty/noise component which is nondeterministic), creating expert and belief-based possibilistic models is cumbersome and subjective. Despite this, different groups of researchers and practitioners favour and do use one of the above approaches with probability theory being (perhaps) the most widely used one. The proposed new framework TEDA is a systematic methodology which does not require prior assumptions and can be used for development of a range of methods for anomalies and fault detection, image processing, clustering, classification, prediction, control, filtering, regression, etc. In this paper due to the space limitations, only few illustrative examples are provided aiming proof of concept.

Słowa kluczowe

EN

data density; proximity measures; RDE; data; analytics; data-driven approaches; machine learning; Bayesian approach

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2014
• Tom: Vol. 8, No. 2
• Strony: 29--35
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Angelov P.

• Intelligent Systems Research Lab, School of Computing and Communications, Lancaster University, LA1 4WA, UK

Bibliografia

• 1. C. Bishop, Machine Learning and Pattern Classification, Springer, 2009.
• 2. D. Osherson, E. E. Smith, “Discussion: On typicality and vagueness”, Cognition, vol. 64, 1997, pp. 189–206.
• 3. P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real time, John Willey, Dec. 2012, ISBN: 978-1-1199-5152-0. DOI:http://dx.doi.org/10.1002/9781118481769
• 4. P. Angelov, Anomalous System State Identification, GB1208542.9 patent application, priority date,15 May 2012.
• 5. J. Iglesias et al., “Creating evolving user behavior profiles automatically”, IEEE Trans. on Knowledge Data Engineering, vol. 24, no. 5, May 2012, pp. 854–867. DOI: http://dx.doi.org/10.1109/TKDE.2011.17
• 6. D. Kolev et al., “ARFA: Automated Real-time Flight Data Analysis using Evolving Clustering, Classifiers and Recursive Density Estimation”. In: Proc. IEEE Symposium Series on Computational Intelligence, SSCI’2013, 16–19 April 2013, Singapore, ISBN 978-1-4673-5855-2/13, pp. 91–97. DOI: http://dx.doi.org/10.1109/EAIS.2013.6604110
• 7. L. A. Zadeh, “Fuzzy sets, Information and Control, vol. 8, no. 3, 1965, pp. 338–353. DOI: http://dx.doi.org/10.1016/S0019-9958(65)90241-X
• 8. P. Angelov, R.Yager, “A New Type of Simplified Fuzzy Rule-based Systems, International Journal of General Systems”, v.41(2): 163-185, Jan. 2012. DOI: http://dx.doi.org/10.1080/03081079.2011.634807
• 9. B. S. J. Costa, P. P. Angelov, L. A. Guedes, “Fully Unsupervised Fault Detection and Identification Based on Recursive Density Estimation and Selfevolving Cloud-based Classifier”, Neurocomputing, 2014, to appear.
• 10. P. Angelov, et al., “Symbol Recognition with a new Autonomously Evolving Classifier AutoClass”, 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS-2014, 2–4 June, 2014, Linz, Austria, to appear.
• 11. http://www.martynhicks.co.uk/weather/data.php?page=m01y2014, accessed 6 February 2014
• 12. P. Angelov, “Fuzzily Connected Multi-Model Systems Evolving Autonomously from Data Streams”, IEEE Transactions on Systems, Man, and Cybernetics – part B, Cybernetics, vol. 41, no. 4,August 2011, pp. 898–910.