Patricia Riddle

Research on particle swarm optimization based clustering: A systematic review of literature and techniques

Abstract Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.

An Evolutionary Particle Swarm Optimization algorithm for data clustering

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas such as finding similarities in images, text data and bio-informatics data. Various optimization techniques have been proposed to improve the performance of clustering algorithms. In this paper we propose a novel algorithm for clustering that we call evolutionary particle swarm optimization (EPSO)-clustering algorithm which is based on PSO. The proposed algorithm is based on the evolution of swarm generations where the particles are initially uniformly distributed in the input data space and after a specified number of iterations; a new generation of the swarm evolves. The swarm tries to dynamically adjust itself after each generation to optimal positions. The paper describes the new algorithm the initial implementation and presents tests performed on real clustering benchmark data. The proposed method is compared with k-means clustering- a benchmark clustering technique and simple particle swarm clustering algorithm. The results show that the algorithm is efficient and produces compact clusters.

A systematic review of web resource estimation

Background: Web development plays an important role in todays industry, so an in depth view into Web resource estimation would be valuable. However a systematic review (SR) on Web resource estimation in its entirety has not been done. Aim: The aim of this paper is to present a SR of Web resource estimation in order to define the current state of the art, and to identify any research gaps that may be present. Method: Research questions that would address the current state of the art in Web resource estimation were first identified. A comprehensive literature search was then executed resulting in the retrieval of 84 empirical studies that investigated any aspect of Web resource estimation. Data extraction and synthesis was performed on these studies with these research questions in mind. Results: We have found that there are no guidelines with regards to what resource estimation technique should be used in a particular estimation scenario, how it should be implemented, and how its effectiveness should be evaluated. Accuracy results vary widely and are dependent on numerous factors. Research has focused on development effort/cost estimation, neglecting other facets of resource estimation like quality and maintenance. Size measures have been used in all but one study as a resource predictor. Conclusions: Our results suggest that there is plenty of work to be done in the field of Web resource estimation whether it be investigating a more comprehensive approach that considers more than a single resource facet, evaluating other possible resource predictors, or trying to determine guidelines that would help simplify the process of selecting a resource estimation technique.

Using Ensembles for Web Effort Estimation

Background: Despite the number of Web effort estimation techniques investigated, there is no consensus as to which technique produces the most accurate estimates, an issue shared by effort estimation in the general software estimation domain. A previous study in this domain has shown that using ensembles of estimation techniques can be used to address this issue. Aim: The aim of this paper is to investigate whether ensembles of effort estimation techniques will be similarly successful when used on Web project data. Method: The previous study built ensembles using solo effort estimation techniques that were deemed superior. In order to identify these superior techniques two approaches were investigated: The first involved replicating the methodology used in the previous study, while the second approach used the Scott-Knott algorithm. Both approaches were done using the same 90 solo estimation techniques on Web project data from the Tukutuku dataset. The replication identified 16 solo techniques that were deemed superior and were used to build 15 ensembles, while the Scott-Knott algorithm identified 19 superior solo techniques that were used to build two ensembles. Results: The ensembles produced by both approaches performed very well against solo effort estimation techniques. With the replication, the top 12 techniques were all ensembles, with the remaining 3 ensembles falling within the top 17 techniques. These 15 effort estimation ensembles, along with the 2 built by the second approach, were grouped into the best cluster of effort estimation techniques by the Scott-Knott algorithm. Conclusion: While it may not be possible to identify a single best technique, the results suggest that ensembles of estimation techniques consistently perform well even when using Web project data.

Particle Swarm Optimization Based Hierarchical Agglomerative Clustering

Clustering- an important data mining task, which groups the data on the basis of similarities among the data, can be divided into two broad categories, partitional clustering and hierarchal. We combine these two methods and propose a novel clustering algorithm called Hierarchical Particle Swarm Optimization (HPSO) data clustering. The proposed algorithm exploits the swarm intelligence of cooperating agents in a decentralized environment. The experimental results were compared with benchmark clustering techniques, which include K-means, PSO clustering, Hierarchical Agglomerative clustering (HAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results are evidence of the effectiveness of Swarm based clustering and the capability to perform clustering in a hierarchical agglomerative manner.

Modelling and synthesising F0 contours with the discrete cosine transform

The discrete cosine transform is proposed as a basis for representing fundamental frequency (F0) contours of speech. The advantages over existing representations include deterministic algorithms for both analysis and synthesis and a simple distance measure in the parameter space. A two-tier model using the DCT is shown to be able to model F0 contours to around 10 Hz RMS error. A proof-of-concept system for synthesising DCT parameters is evaluated, showing that the benefits do not come at the expense of speech synthesis applications.

Exploiting Swarm Behaviour of Simple Agents for Clustering Web Users’ Session Data

In recent years the integration and interaction of data mining and multi agent system (MAS) has become a popular approach for tackling the problem of distributed data mining. The use of intelligent optimization techniques in the form of MAS has been demonstrated to be beneficial for the performance of complex, real time, and costly data mining processes. Web session clustering, a sub domain of Web mining is one such problem, tackling the information comprehension problem of the exponentially growing World Wide Web (WWW) by grouping usage sessions on the basis of some similarity measure. In this chapter we present a novel web session clustering approach based on swarm intelligence (SI), a simple agent oriented approach based on communication and cooperation between agents. SI exploits the collective behaviour of simple agents, cooperation between the agents, and emergence on a feasible solution on the basis of their social and cognitive learning capabilities exhibited in the form of MAS. We describe the technique for web session clustering and demonstrate that our approach perform well against benchmark clustering techniques on benchmark session data.

A swarm intelligence based clustering approach for outlier detection

Outlier detection is an important field in data mining and knowledge discovery, which aims to identify abnormal observations in a large dataset. Common application areas of outlier detection are intrusion detection in computer networks, credit cards fraud detection, detecting abnormal changes in stock prices, and identifying abnormal health conditions. We propose the use of a novel swarm intelligence based clustering technique called Hierarchical Particle Swarm Optimization Based Clustering (HPSO-clustering) for outlier detection. The proposed technique is able to perform Hierarchical Agglom-erative Clustering (HAC) as well as outlier detection. In the proposed approach a swarm of particles evolves through different stages to identify outliers and normal clusters. The experimentation of the proposed approach is performed on benchmark datasets which show that the efficiency of the approach is better than some other popular outlier detection techniques.

DFS* and the Traveling Tournament Problem

Our paper presents a new exact method to solve the traveling tournament problem. More precisely, we apply DFS* to this problem and improve its performance by keeping the expensive heuristic estimates in memory to help greatly cut down the computational time needed. We further improve the performance by exploiting a symmetry property found in the traveling tournament problem. Our results show that our approach is one of the top performing approaches for this problem. It is able to find known optimal solutions in a much smaller amount of computational time than past approaches, to find a new optimal solution, and to improve the lower bounds of larger problem instances which do not have known optimal solutions. As a final contribution, we also introduce a new set of problem instances to diversify the available instance sets for the traveling tournament problem.

An ant colony optimization approach to the traveling tournament problem

The traveling tournament problem has proven to be a difficult problem for the ant colony optimization metaheuristic, with past approaches showing poor results. This is due to the unusual problem structure and feasibility constraints. We present a new ant colony optimization approach to this problem, hybridizing it with a forward checking and conflict-directed backjumping algorithm while using pattern matching and other constraint satisfaction strategies. The approach improves on the performance of past ant colony optimization approaches, finding better quality solutions in shorter time, and exhibits results comparable to other state-of-the-art approaches.