Informatics Publishing Limited

Magesh Kasthuri ¹, V. Shanthi ²

Affiliations
1 SCSVMV University, Kanchipuram, IN
2 Department of MCA, St. Joseph College of Engineering, Chennai, IN

Abstract

This article discusses the concept of Brain Theory, Organization of brain, Techniques behind Artifical Neural Network Recognition and various popular techniques used in Neural network based Patter recognition. The aim of to give idea on Brain theory and its relation and mapping to Artificial Neural Network (ANN) which can guide a Research study on 'Neural networks and Character recognition' to study different techniques used for character recognition and to list the differences between them, the inter-relation between them. Also, I would like to bring out the advantage of each technique with their area of specialization (how easy to use the specified technique for a specific requirement). This will help in easily identify the best suited Handwritten character recognition technique for any requirement (digitizing cheques, palm scripts/manuscripts, extract data from old documents to name a few).

Keywords

ANN-Artificial Neural Networks, Neurons-Unit of Brain for Processing Any Operation, ADLINE-Adaptive Linear Elements, Training-Method for Practicing Neural Network, VTA-Ventral Tegmental Area, ART-Adaptive Resonance Theory, PET-Positron Emission Tomography, LVQ-Learning Vector Quantization.

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E. Manigandan ¹, V. Shanthi ², Magesh Kasthuri ¹

Affiliations
1 SCSVMV University, Enathur, Kanchipuram-631561, Tamil Nadu, IN
2 Department of MCA, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, IN

Abstract

Objectives: Clustering algorithm is broadly used as spectral algorithm in social media, where a reference of contact is used and mined further for various combinations of suggested friends and lookups. This paper Identifies key lead generation techniques to be used in customer relationship management for sales marketing to decide data gathering. Also to define key merits and demerits of these techniques and to prepare a Matrix of comparison of these techniques to justify the data source and data set. M-Cluster algorithm is used for lead qualification i.e. training set preparation and data evaluation. Methods: Todefine a training set based on various attributes/fields in the data given for classification. This training set is used to run the data process and to produce expected result. This is assessed and accepted for definite data set processing or additional run for interim training data set preparation. Findings: This study is taken to customize clustering algorithm for data mining process in a customer relationship management field as the space of data is more and variant. Also proving the usability of customized clustering algorithm in data mining and the efficiency in processing mechanism compared to other methods used in current situation of data mining in customer relationship management is the part of this study. The customized algorithm developed as part of this study considers the two major areas of data mining using clusters. Applications: The results produced tremendous trends that the clustered algorithm suits to any data mining process when scaling and data classification are diversified and less in control.

Keywords

Classification, Clustering Algorithm, Data Mining, K-Means, Lead Generation, M-Clustering Algorithm.

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