Hierarchical Artificial Neural Networks for Recognizing High Similar Large Data Sets

Abstract

This paper proposes a hierarchical artificial neural network for recognizing high similar large data sets. It is usually required to classify large data sets with high similar characteristics in many applications. Analyzing and identifying those data is a laborious task when the methods adopted are primarily based on visual inspection. In many field applications, data sets are measured and recorded continuously using automatic monitoring equipments. Therefore, a large amount of data can be collected, and manual inspection has become an unsuitable approach to recognizing those data. This proposed hierarchical neural network integrates self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks. The SOM networks provide an approximate method for computing the input vectors in an unsupervised manner. Then the computation of the SOM may be viewed as the first stage of the proposed hierarchical network. The second stage is provided by the LVQ networks based on a supervised learning technique that uses class information to improve the quality of the classifier from the first stage. The multistage hierarchical network attempts to factorize the overall input vector into a number of small groups, each of which requires very little computation. Consequently, by use of the proposed network, the loss in accuracy can be small.

Survey on Big Data Mining Algorithms

“…To handle streaming data in real time, a novel algorithm for extracting semantic content were defined in Hierarchical clustering for concept mining [18].This algorithm was designed to be implemented in hardware, to handle data at very high rates. After that the techniques of self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks were discussed in Hierarchical Artificial Neural Networks for Recognizing High Similar Large Data Sets [19]. SOM consumes input in an unsupervised manner whereas LVQ in supervised manner.…”

A comprehensive study on clustering approaches for big data mining

“…SOM accepts unsupervised input while LVQ uses supervised learning. It categorizes large data sets into smaller units, reducing the computational time [14]. More recently, in 2012, Wang used the concepts of physical science to propose a clustering algorithm.…”

Fuzzy partition technique for clustering Big Urban dataset

“…In addition, More techniques like structural coding, frequencies, co-occurrence and graph theory, data reduction techniques, hierarchical clustering techniques, multidimensional scaling were defined in data reduction techniques such as Principle Component Analysis (PCA) for large qualitative dataset to analyse the pattern as required [13]. Two soft computing techniques the Self-Organizing Map (SOM) and learning vector quantization (LVQ) were compared by [15] to categorize large dataset into smaller sets to improve the computation time.…”