M. Ghayekhloo

A novel clustering algorithm based on data transformation approaches

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

A novel soft computing framework for solar radiation forecasting

Display Omitted This paper proposes a new hybrid soft computing framework to increase the solar radiation forecasting accuracy.An improved version of K-means algorithm is proposed to provide fixed, definitive clustering results.A new classification approach is developed to better characterize irregularities and variations of solar radiation.The new method is important for very short-term forecasting where the forecast horizon can be as short as a few seconds. Accurate forecasting of renewable-energy sources plays a key role in their integration into the grid. This paper proposes a novel soft computing framework using a modified clustering technique, an innovative hourly time-series classification method, a new cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to increase the solar radiation forecasting accuracy. The proposed clustering method is an improved version of K-means algorithm that provides more reliable results than the K-means algorithm. The time series classification method is specifically designed for solar data to better characterize its irregularities and variations. Several different solar radiation datasets for different states of U.S. are used to evaluate the performance of the proposed forecasting model. The proposed forecasting method is also compared with the existing state-of-the-art techniques. The comparison results show the higher accuracy performance of the proposed model.

Application of a new binary harmony search algorithm in highway rehabilitation decision-making problems: a case study in Iran

Due to the importance of optimisation in highway rehabilitation projects and the shortcomings of mathematical programming in performing such a task at a quite reasonable runtime span, a new binary harmony search (BHS) is devised to accomplish the decision-making process of highway rehabilitation problems. A new formulation and a BHS are developed and applied to a case study problem consisting of 96 highway segments needing rehabilitation, a number of which should be selected to be rehabilitated at a fixed budget in order to maximise the total quality of the highway network with the minimum possible cost. The BHS provides the value 1 for the segments which must be reconstructed and 0 for the remaining. Pavement Condition Index is employed to assess the quality of the highway. Several sensitivity analyses were performed to examine the influence of different parameters on the output.

A distributed data clustering algorithm in P2P networks

Abstract Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.

NGTSOM: A Novel Data Clustering Algorithm based on Game Theoretic and Self-organizing Map

ABSTRACT: Identifying clusters is an important aspect of data analysis. This paper proposes a novel data clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizing map (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning (CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clustering data. Different strategies of Game Theory are proposed to provide a competitive game for nonwinning neurons to participate in the learning phase and obtain more input patterns. The performance of the proposed clustering analysis is evaluated and compared with that of the K-means, SOM and NG methods using different types of data. The clustering results of the proposed method and existing state-of-the-art clustering methods are also compared which demonstrates a better accuracy of the proposed clustering method. Review History: