Multi Objective Optimization of classification rules using Cultural Algorithms

AbstractClassification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS's) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules

Detection of cartel formation in government biddings using data mining agents

A Controladoria-Geral da União (CGU), como órgão central do sistema de controle interno do Poder Executivo Federal do Brasil é responsável pela realização de atividades de auditoria e fiscalização, visando à prevenção e o combate à corrupção. No entanto, algumas atividades como a detecção de cartéis em licitações é limitada, pela dificuldade de encontrar soluções efetivas em grande volume de bases de dados com milhões de registros de transações financeiras. Nesta seara, algumas áreas de Ciência da Computa-ção apresentam bons resultados no processo de descoberta de conhecimento com uso de técnicas de mineração de dados, tais como classificação, clusterização e regras de associação, as quais, associadas à área de Sistema Multiagente, ampliam o poder de processamento de forma distribuída e interativa com agentes de mineração de dados. Neste sentido, esta pesquisa utiliza agentes de mineração de dados com regras de associação e clusterização para a solução do problema de detecção de cartéis em licita-ções. Como resultado da pesquisa foram descobertas mais de cem regras de associação, das quais dez apresentam fortes indícios de cartelização, comprovando a utilidade da abordagem como suporte ao trabalho de auditoria governamental. __________________________________________________________________________________________ ABSTRACTThe Office of the Comptroller General (CGU), as the central agency of Brazil's Federal Government Internal Control is responsible for the fiscalization and auditing to fight and prevent corruption. However, some activities such as government purchasing fraud detection are limited by the difficulty of finding effective solutions, considering the huge volume of data, with millions of finantial registers. In such a context, the proccess of knowledge discovery may take advantage of Data Mining techniques, including classification, clusterization and association rules; which associated to multiagent system enrich the processing power through the interation and distribuiton of data mining agents. Thus, this research work used data mining agents with association rules and clusterization techniques to identify cartels, acting in fraud detection. As a research result, more than one hundred association rules were discovered, of which ten have strong evidence of cartelization, proving the usefulness of the approach to support the work of government auditing

Multiagent System for Image Mining

The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases

Agent-based Data Integration Framework

Combining data from diverse, heterogeneous sources while facilitating a unified access to it is an important (albeit difficult) task. There are various possibilities of performing it. In this publication, we propose and describe an agent-based framework dedicated to acquiring and processing distributed, heterogeneous data collected from diverse sources (e.g., the Internet, external software, relational, and document databases). Using this multi-agent-based approach in the aspects of the general architecture (the organization and management of the framework), we create a proof-of-concept implementation. The approach is presented using a sample scenario in which the system is used to search for personal and professional profiles of scientists

DETECÇÃO DE CARTÉIS EM LICITAÇÕES PÚBLICAS COM AGENTES DE MINERAÇÃO DE DADOS

The Office of the Comptroller General (CGU), as the central agency of Brazil's Federal Government Internal Control is responsible for the fiscalization and auditing to fight and prevent corruption. However, some activities such as government purchasing fraud detection are limited by the difficulty of finding effective solutions, considering the huge volume of data, with millions of finantial registers. In such a context, the proccess of knowledge discovery may take advantage of Data Mining techniques, including classification, clusterization and association rules; which associated to multiagent system enrich the processing power through the interation and distribuiton of data mining agents. Thus, this research work used data mining agents with association rules and clusterization techniques to identify cartels, acting in fraud detection. As a research result, more than one hundred association rules were discovered, of which ten have strong evidence of cartelization, proving the usefulness of the approach to support the work of government auditing.A Controladoria-Geral da União (CGU), como órgão central do sistema de controle interno do Poder Executivo Federal do Brasil é responsável pela realização de atividades de auditoria e fiscalização, visando à prevenção e o combate à corrupção. No entanto, algumas atividades como a detecção de cartéis em licitações é limitada, pela dificuldade de encontrar soluções efetivas em grande volume de bases de dados com milhões de registros de transações financeiras. Nesta seara, algumas áreas de Ciência da Computação apresentam bons resultados no processo de descoberta de conhecimento com uso de técnicas de mineração de dados, tais como classificação, clusterização e regras de associação, as quais, associadas à área de Sistema Multiagente, ampliam o poder de processamento de forma distribuída e interativa com agentes de mineração de dados. Neste sentido, esta pesquisa utiliza agentes de mineração de dados com regras de associação e clusterização para a solução do problema de detecção de cartéis em licitações. Como resultado da pesquisa foram descobertas mais de cem regras de associação, das quais dez apresentam fortes indícios de cartelização, comprovando a utilidade da abordagem como suporte ao trabalho de auditoria governamental

Agent-based modeling of perishable inventory management using calibrated model-based deep reinforcement learning

Vivimos en un mundo en el que cerca de un 10 % de la población global sufre desnutrición mientras, al mismo tiempo, el 17 % de los alimentos producidos acaban en la basura. La catástrofe ecológica, social y médica que produce este desperdicio es devastadora, y por ello, desde este trabajo queremos aportar nuestro granito de arena para contribuir a paliar esta situación. Parte de esa comida se desperdicia directamente en los supermercados, sin que acabe llegando al consumidor final, provocado por una gestión de inventario ineficiente. Este trabajo ha desarrollado un gestor de inventario de productos perecederos que sea capaz de encargar los pedidos para el día siguiente reduciendo lo máximo posible tanto la comida desperdiciada como las roturas de stock. Para ello se ha modelado un sistema basado en agentes apoyado por sistemas de aprendizaje por refuerzo profundo basado en modelos. Para minimizar el error de este sistema, se han calibrado las incertidumbres de la red neuronal bayesiana que utiliza, usando la técnica de calibración cuantil para regresión

Multi-agent data mining with negotiation: a study in multi-agent based clustering

Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach

The utilisation of games technology for environmental design education

In recent years, the architectural design process has witnessed a mounting demand for qualified practitioners who can resolve the highly complex social, cultural, technological, and economical issues associated with ‘Sustainability’. Designers are thus faced with wider pallet of challenges, developing conceptual designs that are sustainably effective. Pressure is mounting on educational institutions to prepare architects that are well accustomed to the environmental design concepts and parameters, aiming to reduce the impact on the environment and preserve valuable natural resources to bring the building’s interior to comfortable living conditions. However, architectural education has been notably slow to respond effectively to the requirements introduced by sustainability. Evidently there are a number of pedagogical challenges that clearly impede the consistent endorsement of sustainability in the design curricula and thus hinder any potential values and opportunities that can result from its effective integration. This research project examines these challenges and investigates more into their nature and attributes. Accordingly, it proposes a method that endeavours to overcome the noted challenge and attempts to improve the design students' motivation and acceptance to incorporate sustainability. In essence, this method aims to mould the technical nature of Building Performance Simulation applications into the cognitive design process. In order to achieve this, the proposed method utilizes 3D games technology, incorporating Multi-Agent System and Data Mining techniques, to assist design students in achieving higher levels of motivation, engagement, and comprehension of the environmental design concepts. The research discusses the rationale for electing the employed technologies and discusses the methodology for developing the proposed tool. Following its development, the tool is presented to number of stakeholders for evaluating the pedagogical and conceptual basis. The recorded results and the provided feedback from these sessions are presented to assess the potential effectiveness of this method for improving students' understanding of various concepts surrounding sustainable design

The utilisation of games technology for environmental design education

In recent years, the architectural design process has witnessed a mounting demand for qualified practitioners who can resolve the highly complex social, cultural, technological, and economical issues associated with ‘Sustainability’. Designers are thus faced with wider pallet of challenges, developing conceptual designs that are sustainably effective. Pressure is mounting on educational institutions to prepare architects that are well accustomed to the environmental design concepts and parameters, aiming to reduce the impact on the environment and preserve valuable natural resources to bring the building’s interior to comfortable living conditions. However, architectural education has been notably slow to respond effectively to the requirements introduced by sustainability. Evidently there are a number of pedagogical challenges that clearly impede the consistent endorsement of sustainability in the design curricula and thus hinder any potential values and opportunities that can result from its effective integration. This research project examines these challenges and investigates more into their nature and attributes. Accordingly, it proposes a method that endeavours to overcome the noted challenge and attempts to improve the design students' motivation and acceptance to incorporate sustainability. In essence, this method aims to mould the technical nature of Building Performance Simulation applications into the cognitive design process. In order to achieve this, the proposed method utilizes 3D games technology, incorporating Multi-Agent System and Data Mining techniques, to assist design students in achieving higher levels of motivation, engagement, and comprehension of the environmental design concepts. The research discusses the rationale for electing the employed technologies and discusses the methodology for developing the proposed tool. Following its development, the tool is presented to number of stakeholders for evaluating the pedagogical and conceptual basis. The recorded results and the provided feedback from these sessions are presented to assess the potential effectiveness of this method for improving students' understanding of various concepts surrounding sustainable design