Data clustering is a collection of data objects similar to one another within the same cluster and dissimilar to the objects in other clusters. The shuffled frog-leaping algorithm is a nature-inspired algorithm that mimics the natural biological evolution process of frogs. This algorithm also consists of elements like local search and exchanging information globally. This algorithm faces the problem of converging in local optima due to the limitations of the local search method used to explore search space. In this paper, a hybrid shuffled frog-leaping algorithm is introduced for clustering. The proposed algorithm uses a simulated annealing search method instead of a simple local search to improve the search behavior for selecting fitter solutions required in each iteration. Six benchmark datasets are used to validate the performance of the proposed algorithm. Quality measures used are purity, entropy, completeness score (CS), homogeneity score (HS), and FMeasure (FM). Fitness functions used to optimize are total within-cluster variance (TW) and the Silhouette coefficient (SC). Results obtained are compared with the results of twelve other state of the art algorithms. Results stored in the tables clearly shows that our proposed algorithm outperforms other algorithms in terms of quality. Results also prove that the proposed algorithm converges in the significantly less amount of time and eliminates local optima problem also.

No abstract

No abstract

Now a days there is a significant amount of information is present in across the internet in the form of various types more specifically the information on, websites, news, blogs and other digital content. But there is valuable or meaningful information which is hidden inside the data which is very crucial for taking many important decisions. Thus this research is very useful for obtaining such useful information from the available one. The best tool for the extracting the useful information from the available large amount of information is known as data mining. This research deals to get the useful information from large amount of data and which is used in taking the crucial decision. Data mining is the one of the important tool to extract useful and meaningful information from the available large amount of data. Hence data mining is used in most of the applications like healthcare, whether forecasting and entertainment. The importance of data mining in the field of healthcare has proven its importance particularly in preventing, predicting and detecting and also in curing most of the heart diseases should be considered as milestone.