Entropy-based metrics in swarm clustering
Ant-based clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants' propensities to pick up or deposit a data item; however, a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduced an entropy metric into an ant-based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. 
 
In the second phase of the study, we further investigated Ramos and Abraham's ( Proc 2003   2003. pp 1370–1375) contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and nonincremental approaches to data clustering.

title: Incremental Clustering Based on Swarm Intelligence
abstract: We propose methods for incrementally constructing a knowledge model for a dynamically changing database, using a swarm of special agents (ie an ant colony) and imitating their natural cluster-forming behavior. We use information-theoretic metrics to overcome some inherent problems of ant-based clustering, obtaining faster and more accurate results. Entropy governs the pick-up and drop behaviors, while movement is guided by pheromones. The primary benefits are fast clustering, and a reduced parameter set. We compared the method both with static clustering (repeatedly applied), and with the previous dynamic approaches of other authors. It generated clusters of similar quality to the static method, at significantly reduced computational cost, so that it can be used in dynamic situations where the static method is infeasible. It gave better results than previous dynamic approaches, with a much-reduced tuning parameter set. It is simple to use, and applicable to continuously- and batch-updated databases.

title: A Graph-Based Algorithm for Mining Maximal Frequent Itemsets
abstract: Association rule mining is an important research branch of data mining, and computing frequent itemsets is the main problem. The paper is designed to find maximal frequent itemsets only. It presents an algorithm based on a frequent pattern graph, which can find maximal frequent itemsets quickly. A breadth-first-search and a depth-first-search techniques are used to produce all maximal frequent itemsets of a database. The paper also analyzes the complexity of the algorithm, and explains the computation procedure by examples. It has high time efficiency and less space complexity for computing maximal frequent itemsets.

title: Scheduling strategies for mapping application workflows onto the grid
abstract: In this work, we describe new strategies for scheduling and executing workflow applications on grid resources using the GrADS [Ken Kennedy et al., 2002] infrastructure. Workflow scheduling is based on heuristic scheduling strategies that use application component performance models. The workflow is executed using a novel strategy to bind and launch the application onto heterogeneous resources. We apply these strategies in the context of executing EMAN, a bio-imaging workflow application, on the grid. The results of our experiments show that our strategy of performance model based, in-advance heuristic workflow scheduling results in 1.5 to 2.2 times better makespan than other existing scheduling strategies. This strategy also achieves optimal load balance across the different grid sites for this application.

title: An Efficient and Accurate Method for 3D-Point Reconstruction from Multiple Views
abstract: In this paper we consider the problem of finding the position of a point in space given its projections in multiple images taken by cameras with known calibration and pose. Ideally the 3D point can be obtained as the intersection of multiple known rays in space. However, with noise the rays do not meet at a single point generally. Therefore, it is necessary to find a best point of intersection. In this paper we propose a modification of the method (Ma et al., 2001. Journal of Communications in Information and Systems, (1):51–73) based on the multiple-view epipolar constraints. The solution is simple in concept and straightforward to implement. It includes generally two steps: first, image points are corrected through approximating the error model to the first order, and then the 3D point can be reconstructed from the corrected image points using any generic triangulation method. Experiments are conducted both on simulated data and on real data to test the proposed method against previous methods. It is shown that results obtained with the proposed method are consistently more accurate than those of other linear methods. When the measurement error of image points is relatively small, its results are comparable to those of maximum likelihood estimation using Newton-type optimizers; and when processing image-point correspondences cross a small number of views, the proposed method is by far more efficient than the Newton-type optimizers.

title: Classification using support vector machines with graded resolution
abstract: A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.

title: Analyzing and Detecting Review Spam
abstract: Mining of opinions from product reviews, forum posts and blogs is an important research topic with many applications. However, existing research has been focused on extraction, classification and summarization of opinions from these sources. An important issue that has not been studied so far is the opinion spam or the trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews. To our knowledge, there is still no published study on this topic, although Web page spam and email spam have been investigated extensively. We will see that review spam is quite different from Web page spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that review spam is widespread. In this paper, we first present a categorization of spam reviews and then propose several techniques to detect them.

title: Incorporating with Recursive Model Training in Time Series Clustering
abstract: Model-based clustering is one of the most important ways for time series data mining. However, the process of clustering may encounter several problems. In this paper, a novel clustering algorithm of time-series which incorporates recursive hidden Markov model(HMM) training is proposed. Our contributions are as follows: 1) We recursively train models and use these model information in the process agglomerative hierarchical clustering. 2) We built HMM of time-series clusters to describe clusters. To evaluate the effectiveness of the algorithm, several experiments are conducted on both synthetic data and real world data. The result shows that the proposed approach can achieve better performance in correctness rate than the traditional HMM-based clustering algorithm