Manojit Chattopadhyay

Application of visual clustering properties of self organizing map in machine-part cell formation

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, work-in-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using self organizing map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.

Neuro-genetic impact on cell formation methods of Cellular Manufacturing System design: A quantitative review and analysis

This paper presents a quantitative review of the influence and the impact of the two major soft computing approaches, Artificial Neural Network and Genetic Algorithm on cell formation methods of the design of Cellular Manufacturing System (CMS). An in-depth analysis has been carried out to identify the research trend, for the last two decades that captures the chronological progress and continuous improvement in the design of CMS. The in-depth quantitative analysis helped to identify the trend of research, improvements over the years and the capability of the soft-computing approaches to handle complex data-sets with different objective functions. The comparative study of the computational time, number of cells formed and the clustering efficiency obtained, helped to figure out the success rates of each approach and the progress achieved since early 1990s till recent times.

Machine–part cell formation through visual decipherable clustering of self-organizing map

Machine–part cell formation is used in cellular manufacturing in order to process large varieties, improve quality, and lower work-in-process levels, reducing manufacturing lead time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In cellular manufacturing, the fundamental problem is the formation of part families and machine cells. The present paper deals with the self-organizing map (SOM) method, an unsupervised learning algorithm in artificial intelligence which has been used as a visually decipherable clustering tool of machine–part cell formation. The objective of the paper is to cluster the binary machine–part matrix through visually decipherable cluster of SOM color coding and labeling via the SOM map nodes in such a way that the part families are processed in that machine cell. The U-matrix, component plane, principal component projection, scatter plot, and histogram of SOM have been reported in the present work for the successful visualization of the machine–part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and was found to be immensely useful to both industry practitioners and researchers.

PRINCIPAL COMPONENT ANALYSIS AND SELF-ORGANIZING MAP FOR VISUAL CLUSTERING OF MACHINE-PART CELL FORMATION IN CELLULAR MANUFACTURING SYSTEM

The present paper attempts to generate visual clustering and data extraction of cell formation problem using both principal component analysis (PCA) and self-organizing map (SOM) from input of sequence based on the machine-part incidence matrix. Firstly, the focus is to utilize PCA for extracting high-dimensionality of input variables and project the dataset onto a 2D space. Secondly, the unsupervised competitive learning of SOM algorithm is used for data visualization and subsequently, to solve cell formation problem based on ordinal sequence data via the node cluster on the SOM map. Although the numerically illustrated results from dataset revealed that PCA has explained most of the cumulative variance of data but in reality, when the very large-dimensional cell formation problem based on sequence is available then, obtaining the clustering structure from PCA projection becomes very difficult. Most importantly, in the visual clustering of ordinal data, the use of U-matrix alone cannot be efficient to get the cluster structure but with color extraction, hit map, labeling via the SOM node map it becomes a powerful clustering visualization methodology and thus, the present research contribute significantly in the research of cellular manufacturing.

Examining mobile based payment services adoption issues: A new approach using hierarchical clustering and self-organizing maps

Purpose – The purpose of this paper is to make a unified approach in identifying the issues affecting usage intention of mobile-based payment services. The work aims to analyze the reduced factors from data obtained from a survey to highlight the influencers of usage intention mobile-based payment in an integrated manner by incorporating the technical characteristics, technology-specific characteristics, user-specific characteristics and task-specific characteristics. Design/methodology/approach – A nationwide primary survey was conducted using validated questionnaire requesting response for 11 factors obtained from published literature. In all, 196 respondents participated in the survey in India. Valid responses were analyzed using Growing Hierarchical Self-Organizing Map (GHSOM) model. The interactive GHSOM application was applied to automatically determine the filtering rules for clustering. Findings – The hierarchical structure of clusters as obtained by applying GHSOM is mainly influenced by factors ...

The nexus between food price inflation and monsoon rainfall in India: exploring through comparative data mining models

In the paper we analysed the impact on food inflation of monsoon rainfall using different data mining tools, namely: lda (Linear Discriminant Analysis), qda (Quadratic Discriminant Analysis), lr (logistic regression), rpart (Recursive Partitioning and Regression Trees), knn (k-Nearest Neighbors Network), and formulated the models in such a way that food inflation at the end of the financial year can be predicted from the rainfall received during the monsoon month of the year, and a few other known variables. The study is expected to be useful as it can predict the chances of high food inflation with 65% and 63% of accuracy by rpart and lr models, respectively. This information on the chances of high food inflation just after monsoon months can be very useful for policy-makers. While prediction of high food inflation will not in itself solve the problem, it would help decision-makers to take precautionary measures to minimize its adverse impacts on the population.

Identifying periods of market inefficiency for return predictability

ABSTRACT The article examines the efficiency of 31 stock index series spanning 26 countries across the world, using generalized spectral test (GST) and detects departure from the martingale difference hypothesis (MDH). A moving window of 24 months was used and p-values of GST were estimated. In order to explore whether the departure from market efficiency can be used for generating profitable trades, an exponentially weighted-moving-average-based trading rule was applied and was found that average profits per trade were significantly higher when p-value of the GST was less than 0.1. These observations are in consistent with the adapted market hypothesis.

Visual hierarchical clustering of supply chain using growing hierarchical self-organising map algorithm

The study identifies a need for efficient and robust visual clustering approach that can potentially deal with complex supply chain clustering problems. Based on the underlying philosophy of group technology, a growing hierarchical self-organising map algorithm (GHSOM) is proposed to identify a lower two-dimension visual clustering map that can effectively address supply chain clustering problems. The proposed approach provides optimal solutions by decomposing a large-sized supply chain problem into independent, small, manageable problems. It facilitates simple decision-making by exploring similar clusters that are represented by the neighbouring branches in the GHSOM map structure. Unlike other approaches in literature, the proposed approach can further attain good topological ordered representations of the various work order families, to be processed by clusters of supply units along with information on hierarchical sub-cell formation as identifiable from the visually navigable map. The proposed approach has been successfully applied on 16 benchmarked problems. The performance of GHSOM based on grouping efficacy measure outperformed the best results in literature.

Towards Reliable Clustering of English Text Documents Using Correlation Coefficient

This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated according to the conventional TF-IDF factor. The proposed method is found to be able to cluster a given set of text documents into a number of classes depending on their contents where the number of classes is not known a priori. It is revealed from experimental results that the proposed method of text categorization using correlation coefficient performs better than some of the other text categorization methods, including methods that use artificial neural network.