Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets

Publisher's version/PDFWeb usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. The likelihood of bad or incomplete web usage data is higher than the conventional applications. The clusters and associations in web usage mining do not necessarily have crisp boundaries. Researchers have studied the possibility of using fuzzy sets in web mining clustering applications. Recent attempts have adapted the K-means clustering algorithm as well as genetic algorithms based on rough sets to find interval sets of clusters. The genetic algorithms based clustering may not be able to handle large amounts of data. The K-means algorithm does not lend itself well to adaptive clustering. This paper proposes an adaptation of Kohonen self-organizing maps based on the properties of rough sets, to find the interval sets of clusters. Experiments are used to create interval set representations of clusters of web visitors on three educational web sites. The proposed approach has wider applications in other areas of web mining as well as data mining

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

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...

Temporal mining of the web and supermarket data using fuzzy and rough set clustering

xviii, 117 leaves : ill. (some col.) ; 28 cm.Includes abstract.Includes bibliographical references (leaves 114-117).Clustering is an important aspect of data mining. Many data mining applications tend to be more amenable to non-conventional clustering techniques. In this research three clustering methods are employed to analyze the web usage and super market data sets: conventional, rough set and fuzzy methods. Interval clusters based on fuzzy memberships are also created. The web usage data were collected from three educational web sites. The supermarket data spanned twenty-six weeks of transactions from twelve stores spanning three regions. Cluster sizes obtained using the three methods are compared, and cluster characteristics are analyzed. Web users and supermarket customers tend to change their characteristics over a period of time. These changes may be temporary or permanent. This thesis also studies the changes in cluster characteristics over time. Both experiments demonstrate that the rough and fuzzy methods are more subtle and accurate in capturing the slight differences among clusters