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Lukasz A. Kurgan, Krzysztof J. Cios, "CAIM Discretization Algorithm," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 2, pp. 145153, February, 2004.  
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@article{ 10.1109/TKDE.2004.1269594, author = {Lukasz A. Kurgan and Krzysztof J. Cios}, title = {CAIM Discretization Algorithm}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {16}, number = {2}, issn = {10414347}, year = {2004}, pages = {145153}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.1269594}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  CAIM Discretization Algorithm IS  2 SN  10414347 SP145 EP153 EPD  145153 A1  Lukasz A. Kurgan, A1  Krzysztof J. Cios, PY  2004 KW  Supervised discretization KW  classattribute interdependency maximization KW  classification KW  CLIP4 machine learning algorithm. VL  16 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—The task of extracting knowledge from databases is quite often performed by machine learning algorithms. The majority of these algorithms can be applied only to data described by discrete numerical or nominal attributes (features). In the case of continuous attributes, there is a need for a discretization algorithm that transforms continuous attributes into discrete ones. This paper describes such an algorithm, called CAIM (classattribute interdependence maximization), which is designed to work with supervised data. The goal of the CAIM algorithm is to maximize the classattribute interdependence and to generate a (possibly) minimal number of discrete intervals. The algorithm does not require the user to predefine the number of intervals, as opposed to some other discretization algorithms. The tests performed using CAIM and six other stateoftheart discretization algorithms show that discrete attributes generated by the CAIM algorithm almost always have the lowest number of intervals and the highest classattribute interdependency. Two machine learning algorithms, the CLIP4 rule algorithm and the decision tree algorithm, are used to generate classification rules from data discretized by CAIM. For both the CLIP4 and decision tree algorithms, the accuracy of the generated rules is higher and the number of the rules is lower for data discretized using the CAIM algorithm when compared to data discretized using six other discretization algorithms. The highest classification accuracy was achieved for data sets discretized with the CAIM algorithm, as compared with the other six algorithms.
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