Data Mining and Optimization 

Abstract: Microarrays are repositories of gene expression data that hold tremendous potential for new understanding, leading to advances in functional genomics and molecular biology. Cluster analysis (CA) is an early step in the exploration of such data that is useful for purposes of data reduction, exposing hidden patterns, and the generation of hypotheses regarding the relationship between genes and phenotypes. In this paper we present a new model for the clique partitioning problem and illustrate how it can be used to perform cluster analysis in this setting.

Abstract: The University Course Timetabling is a complex optimization Problem which is difficult to solve for optimality. It involves assigning lectures to a fixed number of timeslots and rooms; while satisfying some constraints. The goal is to construct a feasible timetable and satisfy soft constraints as much as possible. In this study, we apply two hybrids Ant Colony Systems, namely the Simulated Annealing with Ant Colony System (ACS-SA), and Tabu Search with Ant Colony System (ACS-TS) to solve the university course timetabling, a number of ants in the ACS construct a complete assignment of courses to timeslots. Based on a pre-ordered list of courses, the ants probabilistically choose the timeslot for the given course, guided by heuristic information and stigmergic information. We test both ACS algorithms over the Socha's benchmark course timetabling problem. We also compare our results with those obtained by other methodologies recent literature has illustrated. Experimental results showed that both ACS-SA and ACS-TS produces good quality solutions and outperforms previously applied Ant algorithms; they also outperform other methodologies tested on Socha's benchmark test instances, and approaches on some benchmark instances. We believe that these hybrid ACS algorithms are also valid for other types of combinational optimization problems.

Abstract: In this paper, we present an efficient algorithm for cluster analysis, which is based on gravitational search and a heuristic search algorithm. In the proposed algorithm, called GSA-HS, the gravitational search algorithm is used to find a near optimal solution for clustering problem, and then at the next step a heuristic search algorithm is applied to improve the initial solution by searching around it. Four benchmark datasets are used to evaluate and to compare the performance of the presented algorithm with two other famous clustering algorithms, i.e. K-means and particle swarm optimization algorithm. The results show that the proposed algorithm can find high quality clusters in all the tested datasets.

Abstract: In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.