An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Profiles of social networking sites users in the Netherlands

Online social networking has become a reality and integral part of the daily personal, social and business life. The extraordinary increase of the user numbers of Social Networking Sites (SNS) and the rampant creation of online communities presents businesses with many challenges and opportunities. From the commercial perspective, the SNS are an interesting and promising field: online social networks are important sources of market intelligence and also offer interesting options for co-operation, networking and marketing. For SMEs especially the Social Networking Sites represent a simple and low cost solution for listening the customer’s voice, reaching potential customers and creating extensive business networks. This paper presents the results of a national survey mapping the demographic, social and behavioral characteristics of the Dutch users of SNS. The study identifies four different user profiles and proposes a segmentation framework as basis for better understanding the nature and behavior of the participants in online communities. The findings present new insights to marketing strategists eager to use the communication potential of such communities; the findings are also interesting for businesses willing to explore the potential of online networking as a low cost yet very efficient alternative to physical, traditional networking

A Retail Outlet Classification Model Based on AdaBoost

This paper proposes a framework to get a stable classification rule under unsupervised learning, and the term ‘‘stable’’ means that the rule remains unchanged when the sample set increases. This framework initially makes use of clustering analysis and then use the result of clustering analysis as a referencestudying sample. Secondly, AdaBoost integrated several classification methods is used to classify the samples and get a stable classification rule. To prove the method feasible, this paper shows an empirical study of classifying retail outlets of a tobacco market in a city of China. In this practice, k-means is used to make clustering analysis, and AdaBoost integrated RBF neural network, CART, and SVM is used in classification. In the empirical study, this method successfully divides retail outlets into different classes based on the sales ability.This work has been partially supported by the National University Student Innovation Program of China and the National Natural Science Foundation of China (Grant No. 61272003)

Machine learning approaches for determining effective seeds for k -means algorithm

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead of the neural network. Both NN and GA used in this study are in their simplest-possible forms. In the simulated data sets, I investigate two properties: clustering performance comparisons and effects of five factors (scale, sample size, density, number of clusters, and number of variables) on the five clustering approaches (KM, NN, NK, GA, GK). Density, number of clusters, and dimension influence the clustering performance of all five approaches. KM, NK, and GK classify well when all clusters contain a similar number of observations, while NK and GK perform better than the KM. NN performs well when one cluster contains more observations than any other cluster. The two hybrid models perform at least as well as KM, although the environments are in favor of the KM. The most crucial information, the true number of clusters, is provided to the KM only. In addition, the cluster structures are simple: the clusters are well separated; the variances and cluster sizes are uniform; the correlation between any pair of variables and collinearity problems are not significant; and the observations are normally distributed. Real-life problems consist of three problems with a known natural cluster structure and one problem with an unknown natural cluster structure. Overall results indicate that GK performs better than KM, while NK is the worst performing among the five approaches. The two machine learning approaches generate better results than KM in an environment that does not favor KM. GK has shown to be the best or among the best in a simulated environment and in real-life situations. Furthermore, the GK can detect firms with promising financial prospect such as acquisition targets and firms with “buy” recommendation, better than all other approaches

Understanding Customer Preferences Using Image Classification – A Case Study

Today, companies have a large amount of data at their disposal. In addition to classic data in text or table form, the number of images also increases enormously. This is particularly the case if the customer contact exists via the Internet, e.g., social networks, blogs or forums. If these images can be evaluated, they lead to a better understanding of the customer. Improved recommendations can be made and customer satisfaction can be increased. This paper shows by means of support vector machines (SVM), convolutional neural networks (CNN) and cluster analyses how it is possible for companies to evaluate image data on their own and thus to understand and classify the customer. The data of travel platform users serve as a case study. Advantages and disadvantages of, as well as prerequisites for SVMs and CNNs are pointed out and segmentation of the users on the basis of their images is made

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Market Segmentation of Leisure Boats Exhibited in the Boat Show by Using Multivariate Statistical Techniques

The aim of this study is to segment recreational boats according to their basic parameters in order to develop marketing strategies and to investigate the benefit/cost factors in consumer preferences across segments. For this purpose, 69 recreational boats under 10 meters exhibited at the Istanbul Boat Show were clustered using basic parameters. In the study, in which hierarchical clustering and multidimensional scaling analysis were used, the boats were divided into four clusters and these results were intended to create an input in the marketing strategies of the boats. These clusters are labelled from the lowest segment to the highest segment, A, B, C and D in ascending order. Based on the calculated averages of these segments for five variables, their intended use is introduced. This segmentation provides guiding findings in different areas such as marketing, advertising and production strategies from the arrangement of the boats within the fair. In addition, alternative actions have been determined for both the customer and the seller by revealing the costs to be incurred in the event that customers prefer different segments