Data Mining - Concepts and Techniques.

The goal of this paper is to propose a dual version of the direct cosine simplex algorithm (DDCA) for general linear problems. The proposed method has not artificial variables, so it is different from both the two-phase method and big-M method. Our technique solves the dual Klee–Minty problem via two iterations and solves the dual Clausen problem via four iterations. The power of the proposed algorithm is evident from the extensive experimental results on benchmark problems adapted from NETLIB. Preliminary results indicate that this dual direct cosine simplex algorithm (DDCA) reduces the number of iterations of the two-phase method.

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On a Dual Direct Cosine Simplex Type Algorithm and Its Computational Behavior

Badr et al. [1] proposed efficient scaling techniques EST with support vector machine on the data set Wisconsin from UCI machine learning with a total 569 rows and 33 columns. In this work, we try to evaluate the validity of the results reached by Badr et al. [1] in the case of using different datasets, different classifiers and dimensionality reduction tools? So, the decision tree algorithm is applied on the used breast cancer microarray dataset (BCMD) contains 289 patients and 35981 attributes. We use principal components analysis (PCA) to reduce the number of attributes. We also propose new scaling techniques to improve the accuracy of the decision tree algorithm. Experimental results show that the decision tree algorithm with new scaling techniques (equilibration, geometric mean and arithmetic mean) achieves 84.98 %, 80.65 % and 79.96 % accuracy against to the traditional normalization (normalization [0, 1], normalization [-1, 1] and standard normalization) by 75.44 %, 76.85% and 78.93%. General Terms Data Mining, Classification

/pdf/breast-cancer-microarray-dataset-with-the-decision-tree-2ezq1917ii.pdf

Naglaa Shehata

Papers

Breast Cancer Microarray Dataset with the Decision Tree Classifier and Efficient Scaling Techniques