Fuzzy clustering of time series data: A particle swarm optimization approach

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because of different applications, the problem of clustering the time series data has become highly popular and many algorithms have been proposed in this field. Recently Swarm Intelligence (SI) as a family of nature inspired algorithms has gained huge popularity in the field of pattern recognition and clustering. In this paper, a technique for clustering time series data using a particle swarm optimization (PSO) approach has been proposed, and Pearson Correlation Coefficient as one of the most commonly-used distance measures for time series is considered. The proposed technique is able to find (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method, a singular value decomposition (SVD) representation of cluster centers is considered. Experimental results over three popular data sets indicate the superiority of the proposed technique in comparing with fuzzy C-means and fuzzy K-medoids clustering techniques

Novel domino procedures for the synthesis of chromene derivatives and their isomerization

Novel tricyclic keto diesters have been synthesized by a one-pot three-component procedure via DABCO-catalyzed domino Knoevenagel–Michael addition reactions. Also, an efficient four-component reaction for the synthesis of another new group of tricyclic keto diesters has been developed via domino Knoevenagel-intramolecular oxo-Diels–Alder reactions. A selective thermal isomerization of the synthesized chromenes to fumarates is also described. X-ray analyses confirm unambiguously the structures of the products

Synthesis of Fluorescent 2,6-Dicyano-3,5-Disubstituted Anilines Using Cellulose Sulfuric Acid in Aqueous Media

The synthesis of some fluorescent 2,6-dicyano-3,5-disubstituted anilines using cellulose sulfuric acid (Cellulose-SA) as an environmentally benign catalyst in H2O is described. The one-pot reaction of 1,3-diketone and three equiv. of malononitrile was carried out in the presence of one equiv. of a secondary amine, Cellulose-SA as catalyst, and H2O as solvent. The photophysical properties (λAbs., λFlu.) of the synthesized compounds in CH2Cl2, MeCN, and MeOH have been measured. The emission spectra of the new compounds in the solid state are also reported

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms