Title :
Amoeba-Based Knowledge Discovery System
Author :
Munakata, Toshinori ; Aono, Masashi ; Hara, Masahiko
Author_Institution :
Comput. & Inf. Sci. Dept., Cleveland State Univ., Cleveland, OH, USA
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.
Keywords :
biology computing; data mining; parallel processing; problem solving; amoeba based knowledge discovery system; amoeboid organism; associated control system; autonomous meta problem solving; data mining system; genetic algorithms; neural networks; parallel computing; Chaos; Computer interfaces; Concurrent computing; Control systems; Data mining; Neural networks; Optical computing; Optical control; Organisms; Parallel processing; Amoeba-based computing; data mining; knowledge discovery; new computing paradigm;
Conference_Titel :
Computational Science and Optimization (CSO), 2010 Third International Joint Conference on
Conference_Location :
Huangshan, Anhui
Print_ISBN :
978-1-4244-6812-6
Electronic_ISBN :
978-1-4244-6813-3
DOI :
10.1109/CSO.2010.88