Amoeba-based knowledge discovery system

Toshinori Munakata, Masashi Aono, Masahiko Hara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose an amoeba-based knowledge discovery or data mining system, that is implemented using an amoeboid organism and an associated control system. The amoeba system can be considered as one of the new non-traditional computing paradigms, and it can perform intriguing, massively parallel computing that utilizes the chaotic behavior of the amoeba. Our system is a hybrid of a traditional knowledge-based unit implemented on an ordinary computer and an amoeba-based search unit, with an interface of an optical control unit. The solutions in our system can have one-to-one mapping to solutions of other well known areas such as neural networks and genetic algorithms. This mapping feature allows the amoeba to use and apply techniques developed in other areas. Various forms of knowledge discovery processes are introduced. Also, a new type of knowledge discovery technique, called "autonomous meta-problem solving," is discussed.

Original languageEnglish
Title of host publication3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010
Subtitle of host publicationTheoretical Development and Engineering Practice
Pages128-131
Number of pages4
Volume1
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice - Huangshan, Anhui, China
Duration: 2010 May 282010 May 31

Other

Other3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice
CountryChina
CityHuangshan, Anhui
Period10/5/2810/5/31

Fingerprint

Knowledge Discovery
Data mining
Unit
Network Algorithms
Parallel processing systems
Chaotic Behavior
Parallel Computing
Knowledge-based
Data Mining
Genetic algorithms
Paradigm
Control System
Genetic Algorithm
Neural Networks
Neural networks
Control systems
Computing

Keywords

  • Amoeba-based computing
  • Data mining
  • Knowledge discovery
  • New computing paradigm

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Control and Optimization
  • Theoretical Computer Science

Cite this

Munakata, T., Aono, M., & Hara, M. (2010). Amoeba-based knowledge discovery system. In 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice (Vol. 1, pp. 128-131). [5532961] https://doi.org/10.1109/CSO.2010.88

Amoeba-based knowledge discovery system. / Munakata, Toshinori; Aono, Masashi; Hara, Masahiko.

3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice. Vol. 1 2010. p. 128-131 5532961.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Munakata, T, Aono, M & Hara, M 2010, Amoeba-based knowledge discovery system. in 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice. vol. 1, 5532961, pp. 128-131, 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice, Huangshan, Anhui, China, 10/5/28. https://doi.org/10.1109/CSO.2010.88
Munakata T, Aono M, Hara M. Amoeba-based knowledge discovery system. In 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice. Vol. 1. 2010. p. 128-131. 5532961 https://doi.org/10.1109/CSO.2010.88
Munakata, Toshinori ; Aono, Masashi ; Hara, Masahiko. / Amoeba-based knowledge discovery system. 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice. Vol. 1 2010. pp. 128-131
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