Rough set approach for categorical data clustering

A few techniques of rough categorical data clustering exist to group objects having similar characteristics. However, the performance of the techniques is an issue due to low accuracy, high computational complexity and clusters purity. This work proposes a new technique called Maximum Dependency Attributes (MDA) to improve the previous techniques due to these issues. The proposed technique is based on rough set theory by taking into account the dependency of attributes of an information system. The main contribution of this technique is to introduce a new technique to classify objects from categorical datasets which has better performance as compared to the baseline techniques. The algorithm of the proposed technique is implemented in MATLAB® version 7.6.0.324 (R2008a). They are executed sequentially on a processor Intel Core 2 Duo CPUs. The total main memory is 1 Gigabyte and the operating system is Windows XP Professional SP3. Results collected during the experiments on four small datasets and thirteen UCI benchmark datasets for selecting a clustering attribute show that the proposed MDA technique is an efficient approach in terms of accuracy and computational complexity as compared to BC, TR and MMR techniques. For the clusters purity, the results on Soybean and Zoo datasets show that MDA technique provided better purity up to 17% and 9%, respectively. The experimental result on supplier chain management clustering also demonstrates how MDA technique can contribute to practical system and establish the better performance for computation complexity and clusters purity up to 90% and 23%, respectively

Fuzzy-rough set and fuzzy ID3 decision approaches to knowledge discovery in datasets

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets mainly concentrate on the construction of approximation operators. Less effort has been put on the knowledge discovery in datasets with fuzzy rough sets. This paper mainly focuses on knowledge discovery in datasets with fuzzy rough sets. After analyzing the previous works on knowledge discovery with fuzzy rough sets, we introduce formal concepts of attribute reduction with fuzzy rough sets and completely study the structure of attribute reduction

A new extension of fuzzy sets using rough sets: R-fuzzy sets

This paper presents a new extension of fuzzy sets: R-fuzzy sets. The membership of an element of a R-fuzzy set is represented as a rough set. This new extension facilitates the representation of an uncertain fuzzy membership with a rough approximation. Based on our definition of R-fuzzy sets and their operations, the relationships between R-fuzzy sets and other fuzzy sets are discussed and some examples are provided

Survey of Rough and Fuzzy Hybridization

In this research existing barriers and the influence of product’s functional lifecycle on the adoption of circular revenue models in the civil and non-residential building sector was investigated. A revenue model, i.e. how revenues are generated in a business model, becomes circular if it is used to extend producer responsibility to create financial incentives for producers to benefit from making their product more circular. For example, leasing or a buy-back scheme in theory creates an incentive for producers to, amongst others, make the product last longer, to be maintained more easily and to be returned. In the Dutch national policy documents there is a call for the development of circular revenue models to extend producer responsibility in the construction sector, as the construction sector is highlighted as a key sector in terms of environmental impact. Adopting circular revenue models in the construction has so far not been research, however expectations about barriers towards adopting circular revenue models can be derived from related literature. The civil and non-residential building sub-sector of the construction sector is of special interest as this subsector has specific characteristics that were expected to create barriers towards adopting circular revenue models: ownership rights and the long functional lifecycle of products (e.g. buildings). This led to the main research question: “What are the barriers to the adoption of circular revenue models in the civil- and non-residential building sector?” The long functional lifecycle of buildings is of special interest as literature suggests that buildings are made from products with different functional lifecycles. This led to led to an additional sub question: “What is the influence of product’s functional lifecycle on the adoption of circular revenue models in the civil and non-residential building sector?” To answer both research questions, the research was split up into three phases. First, semi-structured interviews were held with practitioners, e.g. companies that have adopted, or are working on adopting, circular revenue models. Based upon the results, a second round of interviews was held with experts to better understand the barriers and gather more in-depth insights. The topics chosen for this round were based on the results from the practitioners. The third research phase was a focus group session held primarily with respondents from the expert and practitioner interviews. During the focus group preliminary results were presented and several topics were discussed. During this research 25 barriers, such as a maximum duration for contracts, short-term thinking and the adoption of measurement methods, towards adopting circular revenue models in the civil and non-residential building sector were found, which fit under five main categories in order of importance: financial, sector-specific, regulatory, organisational and technical barriers. Furthermore, seven additional barriers were found when adopting circular revenue models in which producers retain ownership. This shows that there are many barriers that hinder the adoption of circular revenue models in the civil and non-residential building sector, especially when adopting circular revenue models where producers retain ownership. Furthermore, during this research it was found that the shorter the functional lifecycle of building layers, the more easy the adoption of circular revenue models becomes, because, amongst others, financing for longer that 15 years is difficult and two parties to not like to be mutually dependents upon each other over long time periods. In increasing order of difficulty circular revenue models can be adopted to the building layers with longer functional lifecycles: space plan, services, skin and structure. During the research a consensus amongst respondents was identified that circular revenue models should not be adopted to the structure, as the functional lifecycle was too long. In addition to the functional lifecycle, four additional variables were identified that emphasise why the adoption of circular revenue models to building layers with shorter functional lifecycles is more interesting: ratio CAPEX/OPEX, flexibility of products, focus on investor or user and complexity of products

Investigation on soft computing techniques for airport environment evaluation systems

Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture. [Continues.

Geo-rough space

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