Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

Data Mining for Browsing Patterns in Weblog Data by Art Neural Networks

Categorising visitors based on their interaction with a website is a key problem in Web content usage. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customised content. This paper proposes an approach to clustering weblog data, based on ART2 neural networks. Due to the characteristics of the ART2 neural network model, the proposed approach can be used for unsupervised and self-learning data mining, which makes it adaptable to dynamically changing websites.

Chain Split and Computations in Practical Rule Mining

A novel association rule mining algorithm is composed, using the unit cube chain decomposition structures introduced in [HAN, 1966; TON, 1976]. [HAN, 1966] established the chain split theory. [TON, 1976] invented an excellent chain computation framework which brings chain split into the practical domain. We integrate these technologies around the rule mining procedures. Effectiveness is related to the intention of low complexity of rules mined. Complexity of the procedure composed is complementary to the known Apriori algorithm which is defacto standard in rule mining area.

Applying Data Mining Tools in E-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Az európai villamosenergia-árak központi szabályozásának hatása a fogyasztói árakra (The impacts of the price regulation on the level of retail electricity prices in Europe)

Az Európai Unión belül az elmúlt időszakban megerősödött a vita arról, vajon a Közösség versenyképességének javításához milyen módon és mértékben járulhat hozzá az ipari és lakossági fogyasztók számára kedvező áron elérhető villamos energia. Az uniós testületek elsődlegesen a verseny feltételeinek további javításában látják a versenyképesség javításának fő eszközét, ám egyesek az aktívabb központi szabályozás mellett érvelnek. A jelenleg alkalmazott európai szabályozási gyakorlat áttekintése, a szabályozási modellek és a piaci árak alakulásának vizsgálata hozzásegíthet, hogy következtetéseket vonjunk le a tagállami gyakorlatok tekintetében, vajon sikeresebb-e a központi ármegállapításon alapuló szabályozói mechanizmus, mint a liberalizált piacmodell. ______ There is a strengthening debate within the European Union in recent years about the impact of the affordable industrial and household electricity prices on the general competitiveness of European economies. While the European Institutions argues for the further liberalization of the energy retail sector, there are others who believe in centralization and price control to achieve lower energy prices. Current paper reviews the regulatory models of the European countries and examines the connection between the regulatory regime and consumer price trends. The analysis can help to answer, whether the bureaucratic central regulation or the liberalized market model seems more successful in supporting the competitiveness goals. Although the current regulatory practice is heterogeneous within the EU member states, there is a clear trend to decrease the role of regulated tariffs in the end-user prices. Our study did not find a general causal relationship between the regulatory regime and the level of consumer electricity prices in a country concerned. However, the quantitative analysis of the industrial and household energy prices by various segments detected significant differences between the regulated and free-market countries. The first group of member states tends to decrease the prices in the low-consuming household segments through cross-financing technics, including increased network tariffs and/or taxes for the high-consuming segments and for industrial consumers. One of the major challenges of the regulatory authorities is to find the proper way of sharing these burdens proportionally with minimizing the market-distorting effects of the cross-subsidization between the different stakeholder groups.

Project finance in Hungarian electricity sector. Effect of feed-in tariff system’s change onto power plant investments

The research analyses the former and the current status of the small gas-motor power plant investments in the Hungarian energy sector. It discusses the development of project financing in the segment and the major changes and effects of new regulations and subsidy-policy implemented in 2010. The objective of this paper is to present the results of an empirical research of the so called GCHP projects, and to draw conclusion concerning how classic project financing conditions were present and changed during the last decade, and how regulation affected the current and future financial status of these projects.

Mining in Peru: Indigenous and Peasant Communities vs. The State and Mining Capital

The Peruvian economy depends for its growth on the export of natural resources and investment in the mining and hydrocarbon sectors. Peruvian governments and mining corporations have confronted anti-mining protests in different ways. While the current government has introduced policies of social inclusion to soften the negative effects of the operations of mining capital and policies of dialogue to engage social actors with the essence of governmental policies, mining companies use corporate social responsibility programs as a cover for the devastating effects of their operations on the environment and the livelihoods and habitats of the indigenous and peasant communities. Curiously, in the current context of the declining commodity prices and export volumes the Peruvian government strengthens its extractivist model of development. This article argues that whatever government that follows the rules of capital cannot but favor the corporations. It points out the main adversaries of the indigenous and peasant communities and the problems to transform the locally and/or regionally struggle into a nationwide battle for another development model.

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

The Politico-Economics of Electricity Planning in Developing Countries : A Case Study of Ghana

The first author would like to thank the University of Aberdeen and the Henderson Economics Research Fund for funding his PhD studies in the period 2011-2014 which formed the basis for the research presented in this paper.

How effective are heuristic solutions for electricity planning in developing countries

Acknowledgement The first author would like to acknowledge the University of Aberdeen and the Henderson Economics Research Fund for funding his PhD studies in the period 2011-2014 which formed the basis for the research presented in this paper. The first author would also like to acknowledge the Macaulay Development Trust which funds his postdoctoral fellowship with The James Hutton Institute, Aberdeen, Scotland. The authors thank two anonymous referees for valuable comments and suggestions on earlier versions of this paper. All usual caveats apply

Data mining and visualization for anomaly detection and diagnosis in civil structures

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.