Qian Wang^a,b,c, Jiadong Ren^a,b, Darryl N Davis^c, Yongqiang Cheng^c

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 399-404, 2018, DOI:10.1080/10798587.2017.1340135

Abstract Frequent pattern mining usually requires much run time and memory usage. In some applications, only the patterns with top frequency rank are needed. Because of the limited pattern numbers, quality of the results is even more important than time and memory consumption. A Frequent Pattern algorithm for mining Top-rank-K patterns, FP_TopK, is proposed. It is based on a Node-list data structure extracted from FTPP-tree. Each node is with one or more triple sets, which contain supports, preorder and postorder transversal orders for candidate pattern generation and top-rank-k frequent pattern mining. FP_ TopK uses the minimal More >

Marco Götz^1,*, Ferenc Leichsenring¹, Thomas Kropp², Peter Müller², Tobias Falk², Wolfgang Graf¹, Michael Kaliske¹, Welf-Guntram Drossel²

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 387-423, 2018, DOI:10.31614/cmes.2018.04112

Abstract Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The More >

A.S. Koyuncugil¹, N. Ozgulbas²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.2, pp. 39-46, 2009, DOI:10.3970/icces.2009.011.039

Abstract In this paper, a data mining model for detecting financial and operational risk indicators by CHAID Decision Tree is presenting. The identification of the risk factors by clarifying the relationship between the variables defines the discovery of knowledge from the financial and operational variables. Automatic and estimation oriented information discovery process coincides the definition of data mining. During the formation of model; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the More >

H.S. Fadewar¹, S.B. Jagtap¹, G.N.Shinde²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 57-66, 2009, DOI:10.3970/icces.2009.009.057

Abstract Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we propose intelligent data control system design and specifications as an example of DM application in marketing data processing. E-Marketing businesses are using Data Mining to identify patterns in customers' buying behavior; identify profitable customer segments; increase marketing return rates; prevent loss of valuable customers; estimate credit risk; identify fraudulent activity and much more. More >

Y.S. Lian¹, M.S. Liou²

CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.1, pp. 61-72, 2005, DOI:10.3970/cmes.2005.008.061

Abstract This paper focuses on integration of computational methods for design optimization based on data mining and knowledge discovery. We propose to use radial basis function neural networks to analyze the large database generated from evolutionary algorithms and to extract the cause-effect relationship, between the objective functions and the input design variables. The aim is to improve the optimization process by either reducing the computation cost or improving the optimal. Also, it is hoped to provide designers with the salient design pattern about the problem under consideration, from the physics-based simulations. The proposed technique is applied More >