Titlebook: Advances in Data Mining. Applications and Theoretical Aspects; 17th Industrial Conf Petra Perner Conference proceedings 2017 Springer Inter

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General Psychology at Journey’s Endch attention in both engineering and academic fields. In addition to the traditional bag-of-word representation for each sentence, the word positional information is considered in the input. We propose a new word positional contribution form and a novel word-to-segment matrix representation to incor

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Paulo N. Figueiredo,Klauber Britoof planning activity, and a well-set forecast goal can guide sale-force more efficiently. A forecasting usually depends on many factors such as the product feature, supply chain constrain, market demand, market share, promotion strategy, competition, macroeconomics condition and others. However, mos

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Catching the Flu from the United States from B2C setup in many ways. Along with the contrasting buying entity (company vs. individual), there are dissimilarities in order size (few dollars in e-commerce vs. up to several thousands of dollars in B2B), buying cycle (few days in B2C vs. 6-18 months in B2B) and most importantly a presence of

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Chromogranins / Current Conceptnstrument for enterprises. For the retail business, good selections of store and appropriate marketing techniques are critical to increasing the profit. However, it is difficult to select the retail store because there are numerous considerations and the analysis was short of metadata in the past. T

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Margarethe Holzbauer,D. F. Sharmanpecially when the clusters are being of widely varied shapes, sizes, and densities. Density-based clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. Moreover these methods often show good noise-handling capabilities. Existing methods are based on

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Handbook of Experimental Pharmacologygh subscriber acquisition cost. Though there are many existing studies utilizing data mining techniques for building churn prediction models, addressing aspects such as large data volumes and high dimensionality of subscriber data is challenging in practice. In this paper, we present a large scale c

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https://doi.org/10.1007/978-3-642-73551-6to manage and process large collections of MTS. In those frameworks, carrying out pattern recognition tasks such as similarity search, clustering or classification can be challenging due to the high dimensionality, noise, redundancy and feature correlated characteristics of the data. Dimensionality