Class Association Rules Mining based Rough Set Method

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.Comment: 10 pages, 2 figure

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts

In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing ‘hidden’ or not easily discernible data concepts. PLSA provides a ‘bottom up’ approach to interoperability problems for users in the face of ‘top down’ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches

Agglomeration Economies and Heterogeneity within Young Innovative Companies

This paper fits into a new trend in empirical studies on agglomeration economies paying explicit attention to heterogeneity within innovative companies. The paper represents micro-level research, and is based on 21 in-depth case studies in a selected sample of young, innovative companies in the Netherlands. The selection criteria for sampling are derived from resource-based theory, e.g. age, size, corporate position, engaged in services or manufacturing industry. The selected sectors include mechatronics, biotechnology, ICT services and engineering services. In an attempt to identify causal factors and to identify different clusters of companies, we make use of rough set analysis, a method that typically fits small samples and qualitative data. Our research focuses on the importance perceived by company managers of a range of agglomeration advantages for the functioning of the company and on the perceived space in which the company could function satisfactorily. Based on our empirical explorations and given the theoretical positions of the selected case-studies, we arrive at the following findings (1) there is a divide of young, innovative companies into two, namely those facing a high level of importance (in large cities), and those facing a limited importance. In addition, network-based companies that outsource most of their activities to other companies may be facing no importance at all, potentially representing a third category; (2) the strongest factor influencing importance of agglomeration economies is corporate position, e.g. being a corporate spin-off or subsidiary (or not); (3) the spatial influence of agglomeration advantages tends to be broader than large cities only, but there are differences between the individual advantages, e.g. those working in a larger area of central cities, suburban places and medium-sized cities at larger distances, and those exclusively working in large cities or the largest city. Examples of the latter are a pool of young, internationally oriented labour force and direct access to the most advanced telecommunication infrastructure and services. The paper discusses the research design and the empirical outcomes and proposes various new hypotheses to be tested in large scale research.

The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

Geographic Information Systems and Decision Processes for Urban Planning: A Case Study of Rough Set Analysis on the Residential Areas of the City of Cagliari, Italy

In Italy, urban planning is based on the city Masterplan. This plan identifies the future urban organization and a system of zoning rules. Land-use policies are based on these rules. The zoning rules should synthesize environmental and spatial knowledge and policy decisions concerning the possible futures, with reference to the different urban functions. In this essay, a procedure of analysis of the city Masterplan of Cagliari, the regional capital city of Sardinia (Italy), is discussed and applied. This procedure is referred to the residential areas. The procedure tries to explain the urban organization of the housing areas using a system of variables based on the integration of different branches of knowledge concerning the urban environment. The decisions on the urban futures that the zoning rules entail are critically analyzed in terms of consistency with this knowledge system. The procedure consists of two phases. In the first phase, the urban environment is analyzed and described. This is done by defining and developing a geographic information system. This system utilizes a spatial analysis approach to figure out the integration of the residential areas into the urban fabric. The second phase is inferential. Based on the geographic information system developed in the first phase, a knowledge discovery in databases (KDD) technique, the rough set analysis (RSA), is applied. This technique allows to recognize the connection patterns between the urban knowledge system and the city planning decisions. The patterns, the decision rules, which come from the RSA implementation are important starting points for further investigation on the development of decision models concerning urban planning.