8,815 research outputs found

    Clustering stock exchange data by using evolutionary algorithms for portfolio management

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    In present paper, imperialist competitive algorithm and ant colony algorithm and particle swarm optimization algorithm have been used to cluster stocks of Tehran stock exchange. Also results of the three algorithms have been compared with three famous clustering models so called k-means, Fcm and Som. After clustering, a portfolio has been made by choosing some stocks from each cluster and using NSGA-II algorithm. Results show superiority of ant colony algorithms and particle swarm optimization algorithm and imperialist competitive to other three methods for clustering stocks. Due to diversification of the portfolio, portfolio risk will be reduced while using data chosen from the clusters. The more efficient the clustering, the lower the risk is. Also, using clustering for portfolio management reduces time of portfolio selection.peer-reviewe

    Ant colony optimization based clustering for data partitioning.

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    Woo Kwan Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 148-155).Abstracts in English and Chinese.Contents --- p.iiAbstract --- p.ivAcknowledgements --- p.viiList of Figures --- p.viiiList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter Chapter 2 --- Literature Reviews --- p.7Chapter 2.1 --- Block Clustering --- p.7Chapter 2.2 --- Clustering XML by structure --- p.10Chapter 2.2.1 --- Definition of XML schematic information --- p.10Chapter 2.2.2 --- Identification of XML schematic information --- p.12Chapter Chapter 3 --- Bi-Tour Ant Colony Optimization for diagonal clustering --- p.15Chapter 3.1 --- Motivation --- p.15Chapter 3.2 --- Framework of Bi-Tour Ant Colony Algorithm --- p.21Chapter 3.3 --- Re-order of the data matrix in BTACO clustering method --- p.27Chapter 3.3.1 --- Review of Ant Colony Optimization --- p.29Chapter 3.3.2 --- Bi-Tour Ant Colony Optimization --- p.36Chapter 3.4 --- Determination of partitioning scheme --- p.44Chapter 3.4.1 --- Weighed Sum of Error (WSE) --- p.48Chapter 3.4.2 --- Materialization of partitioning scheme via hypothetic matrix --- p.50Chapter 3.4.3 --- Search of best-fit hypothetic matrix --- p.52Chapter 3.4.4 --- Dynamic programming approach --- p.53Chapter 3.4.5 --- Heuristic partitioning approach --- p.57Chapter 3.5 --- Experimental Study --- p.62Chapter 3.5.1 --- Data set --- p.63Chapter 3.5.2 --- Study on DP Approach and HP Approach --- p.65Chapter 3.5.3 --- Study on parameter settings --- p.69Chapter 3.5.4 --- Comparison with GA-based & hierarchical clustering methods --- p.81Chapter 3.6 --- Chapter conclusion --- p.90Chapter Chapter 4 --- Application of BTACO-based clustering in XML database system --- p.93Chapter 4.1 --- Introduction --- p.93Chapter 4.2 --- Overview of normalization and vertical partitioning in relational DB design --- p.95Chapter 4.2.1 --- Normalization of relational models in database design --- p.95Chapter 4.2.2 --- Vertical partitioning in database design --- p.98Chapter 4.3 --- Clustering XML documents --- p.100Chapter 4.4 --- Proposed approach using BTACO-based clustering --- p.103Chapter 4.4.1 --- Clustering XML documents by structure --- p.103Chapter 4.4.2 --- Clustering XML documents by user transaction patterns --- p.109Chapter 4.4.3 --- Implementation of Query Manager for our experimental study --- p.114Chapter 4.5 --- Experimental Study --- p.118Chapter 4.5.1 --- Experimental Study on the clustering by structure --- p.118Chapter 4.5.2 --- Experimental Study on the clustering by user access patterns --- p.133Chapter 4.6 --- Chapter conclusion --- p.141Chapter Chapter 5 --- Conclusions --- p.143Chapter 5.1 --- Contributions --- p.144Chapter 5.2 --- Future works --- p.146Bibliography --- p.148Appendix I --- p.156Appendix II --- p.168Index tables for Profile A --- p.168Index tables for Profile B --- p.171Appendix III --- p.17

    MACOC: a medoid-based ACO clustering algorithm

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    The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

    Finding groups in data: Cluster analysis with ants

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    Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine, and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modifications are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques
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