Perspectives on Intelligent system techniques used in Data Mining

With the uprising trend of the social media, for marketing purposes and for personal communication, the data mining techniques used in the early decade can no longer handle the data for pattern recognition .It becomes necessity that the data mining techniques become intelligent enough to deal with the volume and variety of the data being searched for recognizing patterns or trends. Thus in this article, I have explored the various neural networks and their techniques being implemented on data to find a particular trend. Various applications of such hybrid intelligent systems have been discussed. Perceptron and it's varieties have been long used for machine learning without the human intervention. Moreover their various features are inspired by the biological strategies endowed by nature on humans to recognize, learn and innovative ideas to solve their problems. Humans have various sensory organs to help them receive their inputs from the surroundings, however it is their brain that helps them to process the large data and get the required data as an output. Neural Networks are based on the Human brain and the nervous system. So we shall explore various intelligent systems of Neural Networks to help in data mining.

[1]  Mark S. Leeson,et al.  Pattern recognition of fiber-reinforced plastic failure mechanism using computational intelligence techniques , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[2]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[3]  Chee Peng Lim,et al.  Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility , 2008, Appl. Soft Comput..

[4]  Michael Georgiopoulos,et al.  Properties of learning in ARTMAP , 1994, Neural Networks.

[5]  Jacek M. Zurada,et al.  Knowledge-based neurocomputing , 2000 .

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

[8]  Jianhua Yang.  Intelligent data mining using artificial neural networks and genetic algorithms : techniques and applications , 2010 .

[9]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[10]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[11]  Robert F. Harrison,et al.  Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks , 1996, Artif. Intell. Medicine.

[12]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[13]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[14]  Evor L. Hines,et al.  Classification and feature extraction strategies for multi channel multi trial BCI data , 2007 .

[15]  Servet Soyguder,et al.  An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach , 2009 .

[16]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[17]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[18]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[19]  Nathalie Godin,et al.  Integration of the Kohonen's self-organising map and k-means algorithm for the segmentation of the AE data collected during tensile tests on cross-ply composites , 2005 .

[20]  I. Hatono,et al.  Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.