1002750
Particle Swarm based Data Mining Algorithms for classification tasks
Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.
18811835
An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
In this paper we present and discuss the results of experimentally comparing the performance of several variants of the standard swarm particle optimiser and a new approach to swarm based optimisation. The new algorithm, which we call predator prey optimiser, combines the ideas of particle swarm optimisation with a predator prey inspired strategy, which is used to maintain diversity in the swarm and preventing premature convergence to local suboptima. This algorithm and the most common variants of the particle swarm optimisers are tested in a set of multimodal functions commonly used as benchmark optimisation problems in evolutionary computation.
5929323
Agent-based modelling of product invention
This study describes a novel simulation model of the process of product invention. Invention is conceptualized as a process of directed evolutionary adaptation, on a landscape of product design possibilities, by a population of profit-seeking agents (inventors). The simulation experiments examine the sensitivity of the rate of advance in product fitness to the choice of search heuristics employed by inventors. The key finding of the experiments is that if search heuristics are confined to those which are rooted in past experience, or to heuristics which merely generate variety, limited product advance occurs. Notable product fitness advance only occurs when inventor's expectations as to the relative fitness of potential product inventions are incorporated into the model of invention. The results demonstrate the importance of human direction and expectations in invention. They also support the importance of formal product / project evaluation procedures in organizations, and the importance of market information when inventing new products.
2626702
A Particle Swarm Model of Organizational Adaptation
This study introduces the particle swarm metaphor to the domain of organizational adaptation. A simulation model (OrgSwarm) is constructed to examine the impact of strategic inertia, in the presence of errorful assessments of future payoffs to potential strategies, on the adaptation of the strategic fitness of a population of organizations. The results indicate that agent (organization) uncertainty as to the payoffs of potential strategies has the affect of lowering average payoffs obtained by a population of organizations. The results also indicate that a degree of strategic inertia, in the presence of an election mechanism, assists rather than hampers adaptive efforts in static and slowly changing strategic environments.
6828563
A Particle Swarm Data Miner
This paper describes the implementation of Data Mining tasks using Particle Swarm Optimisers. The object of our research has been to apply such algorithms to classification rule discovery. Results, concerning accuracy and speed performance, were empirically compared with another evolutionary algorithm, namely a Genetic Algorithm and with J48 – a Java implementation of C4.5. The data sets used for experimental testing have already been widely used and proven reliable for testing other Data Mining algorithms. The obtained results seem to indicate that Particle Swarm Optimisers are competitive with other evolutionary techniques, and could come to be successfully applied to more demanding problem domains.
7409552
Swarm optimisation as a new tool for data mining
This paper proposes the use of particle swarm optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the particle optimiser with another evolutionary algorithm, namely a genetic algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for decision support systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that particle swarm optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.
8086424
Dynamic evolution in workflow management systems
The environments where workflow management systems are typically used in are constantly changing. It is nearly impossible to foresee, at business process design stage, all the combinations of tasks needed to achieve the process' goals. Workflow management systems capable of supporting dynamic changes on executing instances in a flexible manner are thereby demanded. Former approaches have proposed a solution to this issue based on workflow type versioning and workflow instance migration. However, the operations that handle the modification of workflow types as well as the migration algorithm for workflow instances are tied to a particular representation of workflow types. This means that whenever the representation changes, the migration algorithms and the modification operations have to change accordingly. This is a current problem due to the uprising of different specifications for workflow definition languages, which are far away from stabilizing. In this paper, we address this problem by clearly decoupling the modification operations of workflow types from the internal structures and algorithms responsible for the instances' migration. In this manner we enable the use of different workflow definition languages while keeping the same internal structures and migration algorithms.
16566221
Naming: design pattern and framework
Naming is an important, but frequently overlooked, aspect of software systems. Naming is usually combined with other issues, e.g. distributed communication technology. The paper treats naming in isolation. It presents a naming design pattern which abstracts several naming policies and an object-oriented framework that can be customized to support naming policies. An applications designer can choose the correct naming properties (relative, absolute, universal identifiers, etc.) to refer to specific objects.
1918071
ESTWeb: bioinformatics services for EST sequencing projects
ESTWeb is an internet based software package designed for uniform data processing and storage for large-scale EST sequencing projects. The package provides for: (a) reception of sequencing chromatograms; (b) sequence processing such as base-calling, vector screening, comparison with public databases; (c) storage of data and analysis in a relational database, (d) generation of a graphical report of individual sequence quality; and (e) issuing of reports with statistics of productivity and redundancy. The software facilitates real-time monitoring and evaluation of EST sequence acquisition progress along an EST sequencing project.
38014527
A Quality Design Solution for Object Synchronization
This paper presents a quality design solution, the Customizable Object Synchronization pattern, for object synchronization which decouples object synchronization from object concurrency and object functionality (sequential part). The solution described by this pattern provides encapsulation, extensibility, modularity and reuse of synchronization policies.
12270268
Mining Web Usage Data for Discovering Navigation Clusters
The analysis of a web site based on usage data is an important task as it provides insight into the organization of the site and its adequacy regarding user needs. This allows the relationship between prior categories and user browsing patterns to be explored. In this paper we propose an approach for discovering the profiles of visitor groups. To this end, we begin by mapping user interests into symbolic objects, which is the basis of the Symbolic Data Analysis and represents here a successful interaction of the user with the web site. We then identify groups of users with similar behavior by means of a dynamic clustering approach applying a context dependent dissimilarity measure. The method was applied to identify visitor groups of a web site in the educational domain and also to analyze the traces of different user behavior.