An Efficient Support Based Ant Colony Optimization Technique for Lung Cancer Data

Lung cancer is one of the most dangerous cancer's type in the world. Early detection can save the life and survivability of the patients. In this paper we want to propose a solution in the direction of lung cancer symptom detection. In this paper we proposed a new algorithm that is support based ant colony optimization technique (SPACO). Our algorithm is broadly divided into three parts, in the first part we accept the data set of cancer symptoms which is a generalized way for creating the patterns for Lung Cancer Framework, and in the second part we find the relevant data from the patterns. We can choose the frequent symptoms only by using the support count value. According to the support value we decide the ants and pheromone value. We initialize the pheromone value which is the support of the pattern of cancer symptoms. It is updated in each trial. By updating the pheromone value in each step we can check the symptom quality which either increases the prediction or decreases the prediction. Finally by result analysis we can prove the effectiveness of our algorithm.

[1]  Amog Rajenderan.  Data Preparation for Web Mining – A survey , 2012 .

[2]  Shyi-Ching Liang,et al.  The application of ant colony optimization to the classification rule problem , 2011, 2011 IEEE International Conference on Granular Computing.

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  Marco Dorigo,et al.  New Ideas in Optimisation , 1999 .

[5]  S.H. Zainud-Deen,et al.  Breast cancer detection using a hybrid Finite difference frequency domain and particle swarm optimization techniques , 2008, 2008 National Radio Science Conference.

[6]  M. H. Mehta.  Hybrid Genetic Algorithm with PSO Effect for Combinatorial Optimisation Problems , 2012 .

[7]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[8]  Ping-Hung Tang,et al.  Medical data mining using BGA and RGA for weighting of features in fuzzy k-NN classification , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[9]  Yao Liu,et al.  Mining cancer data with discrete particle swarm optimization and rule pruning , 2011, 2011 IEEE International Symposium on IT in Medicine and Education.

[10]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[11]  Hnin Wint Khaing.  Data mining based fragmentation and prediction of medical data , 2011, 2011 3rd International Conference on Computer Research and Development.

[12]  Anshuman Singh Sadh,et al.  Association Rules Optimization: A Survey , 2013 .

[13]  Kamran Kiasaleh,et al.  Permittivity estimation for breast cancer detection using particle swarm optimization algorithm , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[15]  Nitin Shukla,et al.  Apriori and Ant Colony Optimization of Association Rules , 2013 .

[16]  David J. Hand,et al.  Discrimination and Classification , 1982 .

[17]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[18]  H. Gupta,et al.  An Optimized Feature Selection for Image Classification Based on SVM- ACO , 2012 .

[19]  Hitesh Gupta,et al.  A Review of Density-Based clustering in Spatial Data , 2012 .