Data Mining as Support to Knowledge Management in Marketing

Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers\u27 profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability

Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach

Although energy efficiency is a hot topic in the context of global climate change, in the European Union directives and in national energy policies, methodology for estimating energy efficiency still relies on standard techniques defined by experts in the field. Recent research shows a potential of machine learning methods that can produce models to assess energy efficiency based on available previous data. In this paper, we analyse a real dataset of public buildings in Croatia, extract their most important features based on the correlation analysis and chi-square tests, cluster the buildings based on three selected features, and create a prediction model of energy efficiency for each cluster of buildings using the artificial neural network (ANN) methodology. The main objective of this research was to investigate whether a clustering procedure improves the accuracy of a neural network prediction model or not. For that purpose, the symmetric mean average percentage error (SMAPE) was used to compare the accuracy of the initial prediction model obtained on the whole dataset and the separate models obtained on each cluster. The results show that the clustering procedure has not increased the prediction accuracy of the models. Those preliminary findings can be used to set goals for future research, which can be focused on estimating clusters using more features, conducted more extensive variable reduction, and testing more machine learning algorithms to obtain more accurate models which will enable reducing costs in the public sector

Knowledge based systems in sales decision making

Rad se bavi modeliranjem sustava zasnovanih na znanju s ciljem povećanja kvalitete odlučivanja u segmentu veleprodaje. Ekspertni sustavi, kao tehnika sustava zasnovanih na znanju, računalni su programi koji se služe znanjem eksperta iz nekoga specijaliziranog područja u svrhu potpore odlučivanju. U radu je dan pregled prethodnih istraživanja upotrebe sustava zasnovanih na znanju u prodaji, kao i mogućnosti njihove prilagodbe potrebama i preferencama korisnika. U cilju provjere tih mogućnosti dizajniran je eskpertni sustav za donošenje odluke o odobrenju prodaje robe određenom kupcu u veleprodaji. Predložena baza znanja ekspertnog sustava koristi se produkcijskim pravilima. S pomoću ljuske programa Exsys Corvid izgrađen je ekspertni sustav koji je u testnoj fazi pokazao da je njegovom uporabom moguće ubrzati proces prodaje unutar tvrtke i povećati učinkovitost prodajnog poslovanja. U radu su analizirane neke prednosti i nedostatci uporabe takvih sustavaThe paper deals with modeling knowledg based systems with the aim of increasing the quality of decision making in the segment of wholesales. Expert systems, as a techinque of knowledge based systems, are computer programs that use the knowledge from an expert in a specialized area in purpose of decision support. A review of previous research of knowledge based systems in sales is given in the paper, as well as the possibilities of their adjustment to the user needs and preferences. In order to test those abilities, an expert system aimed for decision support in the process of selling goods to a certain customer is designed. The suggested knowledge base uses production rules to represent knowledge. By using the expert system shell ExSys Corvid, an expert system is built which showed in its testing phase that it is possible to make the sale process faster and more efficient. Some benefits and limitations of the suggested system are discussed in the paper

Model neuronske mreže za predviđanje ciljne kamatne stope državnih rezervi

Cilj rada bio je kreirati model za predviđanje ciljne kamatne stope federalne banke SAD-a (FED) s pomoću neuronskih mreža. Model je temeljen na podacima o kretanju makroekonomskih varijabli SAD-a u razdoblju od 1959 do 2005. godine. Korišteno je dvanaest ulaznih varijabli, dok je izlazna varijabla bila ciljna kamatna stopa na prekonoćne zajmove kojom FED održava monetarnu stabilnost zemlje. Različite arhitekture neuronskih mreža testirane su pomoću backpropagation algoritma višeslojne perceptron mreže, te je izabran najbolji model na temelju greške na uzorku za testiranje. Provedena je analiza osjetljivosti, na temelju koje je otkriveno da najveći utjecaj na izlaznu varijablu ima promjena cijene zlata, te promjena tržišnih indekasa (Dow Jones i S&P500). Rezultati modeliranja pokazuju da neuronska mreža neupitno uočava i usvaja međuodnose ulaznih i izlaznih varijabli. Kreirani model ukazuje na značajne mogućnosti metoda umjetne inteligencije u području predviđanja kamatnih stopa i može se koristiti za daljnja istraživanja u tom području

SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS

After production and operations, finance and investments are one of the most frequent areas of neural network applications in business. The lack of standardized paradigms that can determine the efficiency of certain NN architectures in a particular problem domain is still present. The selection of NN architecture needs to take into consideration the type of the problem, the nature of the data in the model, as well as some strategies based on result comparison. The paper describes previous research in that area and suggests a forward strategy for selecting best NN algorithm and structure. Since the strategy includes both parameter-based and variable-based testings, it can be used for selecting NN architectures as well as for extracting models. The backpropagation, radialbasis, modular, LVQ and probabilistic neural network algorithms were used on two independent sets: stock market and credit scoring data. The results show that neural networks give better accuracy comparing to multiple regression and logistic regression models. Since it is model-independant, the strategy can be used by researchers and professionals in other areas of application

CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods

COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS

Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (ANNs). PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research

Discovering market basket patterns using hierarchical association rules

Association rules are a data mining method for discovering patterns of frequent item sets, such as products in a store that are frequently purchased at the same time by a customer (market basket analysis). A number of interestingness measures for association rules have been developed to date, but research has shown that there a dominant measure does not exist. Authors have mostly used objective measures, whereas subjective measures have rarely been investigated. This paper aims to combine objective measures such as support, confidence and lift with a subjective approach based on human expert selection in order to extract interesting rules from a real dataset collected from a large Croatian retail chain. Hierarchical association rules were used to enhance the efficiency of the extraction rule. The results show that rules that are more interesting were extracted using the hierarchical method, and that a hybrid approach of combining objective and subjective measures succeeds in extracting certain unexpected and actionable rules. The research can be useful for retail and marketing managers in planning marketing strategies, as well as for researchers investigating this field