Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means Algorithm, namely Persistent K-Means, which alters the convergence method of K-Means Algorithm to provide more accurate clustering results than the K-Means Algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of KMeans Algorithm.

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering Algorithms in many fields, a lot of research is still going on to find the best and efficient clustering Algorithm to partition the data items. K-Means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper, a new hybrid data clustering approach which combines the modified krill herd and K-Means Algorithms, named as K-MKH, is proposed. K-MKH Algorithm utilizes the power of quick convergence behaviour of K-Means and efficient global exploration of Krill Herd and random phenomenon of Levy flight method. The Krill-herd Algorithm is modified by incorporating Levy flight into it to improve the global exploration. The proposed Algorithm is tested on artificial and real life datasets. The simulation results are compared with other methods such as K-Means, Particle Swarm Optimization (PSO), Original Krill Herd (KH), hybrid K-Means and KH. Also the proposed Algorithm is compared with other evolutionary Algorithms such as hybrid modified cohort intelligence and K-Means (K-MCI), Simulated Annealing (SA), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-Means++. The comparison shows that the proposed Algorithm improves the clustering results and has high convergence speed.

University course timetabling problem is one of the hard problems and it must be done for each term frequently which is an exhausting and time consuming task. The main technique in the presented approach is focused on developing and making the process of timetabling common lecturers among different departments of a university scalable. The aim of this paper is to improve the satisfaction of common lecturers among departments and then minimize the loss of resources within departments. The applied method is to use a collaborative search approach. In this method, at first all departments perform their scheduling process locally; then two clustering and traversing agents are used where the former is to cluster common lecturers among departments and the latter is to find unused resources among departments. After performing the clustering and traversing processes, the mapping operation in done based on principles of common lecturers constraint in redundant resources in order to gain the objectives of the problem. The problem’s evaluation metric is evaluated via using fuzzy c-Means clustering Algorithm on common lecturer constraints within a multi agent system. An applied dataset is based on meeting the requirements of scheduling in real world among various departments of Islamic Azad University, Ahar Branch and the success of results would be in respect of satisfying uniform distribution and allocation of common lecturers on redundant resources among different departments.