In recent decades, the use of incentive-based reward programs to foster customer loyalty and promote sales has become prevalent in various industries. While these strategies are widely accepted and implemented, there is a significant gap in empirical studies to ascertain their real-world effectiveness. This thesis embarks on a comprehensive examination into the effectiveness of an online business's reward program, utilizing data from the past five years and employing data mining techniques, including RFM (Recency, Frequency, Monetary) model and clustering algorithms; hypothesis tests are employed to further strengthen the drawn conclusions. Contrary to popular theories, the findings reveal that small incentives such as rewards did not induce significant changes in customer purchasing behavior, nor did they effectively boost sales among rewarded customers. A control group of non-rewarded top-class customers showed more robust purchasing patterns. These unexpected results challenge existing beliefs and call for a critical re-evaluation of current practices in sales promotion and customer loyalty. The research underscores the need for empirically grounded strategies, further exploration into alternative loyalty-building methods, and a recognition of the complex realities influencing customer engagement.

[ES] A partir de información del Censo Agrario Nacional se caracterizaron y tipificaron las Unidades Productivas Agrícolas-UPA hortofrutícolas en el departamento de Antioquia (Colombia), utilizando las características tenidas en cuenta por las políticas, para plantear elementos que contribuyan a una formulación e implementación de política pública diferenciada que atienda las categorías de la tipificación y de acuerdo con su situación particular, en términos de capacidades y limitaciones. Se conformaron índices temáticos, compuestos por diferentes variables, que posteriormente se ponderaron cuantitativamente y fueron utilizados para realizar la clusterización de las UPA con el método de agrupación K-means determinándose 6 clústeres, las características de cada uno fueron analizadas para realizar recomendaciones que mejoren el proceso de implementación de política pública sectorial. Para la formulación de las recomendaciones, se realizó una revisión del inventario actual de las políticas agrícolas y relacionadas, clasificando las mismas mediante el uso de la metodología OCDE-BID de Estimados de Apoyo al Productor. Lo anterior permitió realizar un análisis que relacionó las necesidades de los productores identificadas de las preguntas del censo, con la oferta de política agrícola, como punto de partida para realizar recomendaciones diferenciadas de política para cada uno de los clústeres determinados. / [CA] A partir d'informació del Cens Agrari Nacional es van caracteritzar i van tipificar les Unitats Productives Agrícoles-UPA hortofructícoles en el departament de Antioquia (Colòmbia), utilitzant les característiques tingudes en compte per les polítiques, per a plantejar elements que contribuïsquen a una formulació i implementació de política pública diferenciada que atenga les categories de la tipificació i d'acord amb la seua situació particular, en termes de capacitats i limitacions. Es van conformar índexs temàtics, compostos per diferents variables, que posteriorment es van ponderar quantitativament i van ser utilitzats per a realitzar la clusterización de les UPA amb el mètode d'agrupació K-means determinant-se 6 clústers, les característiques de cadascun van ser analitzades per a realitzar recomanacions que milloren el procés d'implementació de política pública sectorial. Per a la formulació de les recomanacions, es va realitzar una revisió de l'inventari actual de les polítiques agrícoles i relacionades, classificant les mateixes mitjançant l'ús de la metodologia OCDE-BID d'Estimats de Suport al Productor. L'anterior va permetre realitzar una anàlisi que va relacionar les necessitats dels productors dedicades de les preguntes del cens, amb l'oferta de política agrícola, com a punt de partida per a realitzar recomendación diferenciades de política per a cadascun dels clústers determinats. / [EN] Based on information from the National Agricultural Census, the fruit and vegetable agricultural production units (UPAs) in the department of Antioquia (Colombia) have been characterized and typified, using the characteristics taken into account by the policies, in order to propose elements that contribute to the formulation and implementation of a differentiated public policy that addresses the categories of typification and according to their particular situation, in terms of capacities and limitations. Thematic indexes were formed, composed of different variables, which were subsequently quantitatively weighted and used to cluster the UPAs with the K-means grouping method, determining 6 clusters, the characteristics of each of which were analyzed to make recommendations to improve the process of implementing sectoral public policies. For the formulation of the recommendations, a review of the current inventory of agricultural and related policies was carried out, classifying them using the OECD-IDB methodology of Producer Support Estimates. This made it possible to carry out an analysis relating the needs of producers identified in the census questions to the supply of agricultural policies, as a starting point for making differentiated policy recommendations for each of the clusters identified. / Salamanca Sanjuanes, HA. (2024). Las políticas públicas agrarias colombianas frente a las particularidades de las unidades productivas agrícolas: el caso de la hortofruticultura del Departamento de Antioquia [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/213871

The large potential of big data and how it has brought value into various industries have been established in research. Since big data has such large potential if handled and analyzed in the right way, revealing information to support decision making in an organization, this thesis is conducted as a case study at an automotive manufacturer with access to large amounts of customer usage data of their vehicles. The reason for performing an analysis of this kind of data is based on the cornerstones of Total Quality Management with the end objective of increasing customer satisfaction of the concerned products or services. The case study includes a data analysis exploring how and if patterns about what affects fuel consumption can be revealed from aggregated customer usage data of trucks linked to truck applications. Based on the case study, conclusions are drawn about how a company can use this type of analysis as well as how to handle the data in order to turn it into business value. The data analysis reveals properties describing truck usage using Factor Analysis and Principal Component Analysis. Especially one property is concluded to be important as it appears in the result of both techniques. Based on these properties the trucks are clustered using k-means and Hierarchical Clustering which shows groups of trucks where the importance of the properties varies. Due to the homogeneity and complexity of the chosen data, the clusters of trucks cannot be linked to truck applications. This would require data that is more easily interpretable. Finally, the importance for fuel consumption in the clusters is explored using model estimation. A comparison of Principal Component Regression (PCR) and the two regularization techniques Lasso and Elastic Net is made. PCR results in poor models difficult to evaluate. The two regularization techniques however outperform PCR, both giving a higher and very similar explained variance. The three techniques do not show obvious similarities in the models and no conclusions can therefore be drawn concerning what is important for fuel consumption. During the data analysis many problems with the data are discovered, which are linked to managerial and technical issues of big data. This leads to for example that some of the parameters interesting for the analysis cannot be used and this is likely to have an impact on the inability to get unanimous results in the model estimations. It is also concluded that the data was not originally intended for this type of analysis of large populations, but rather for testing and engineering purposes. Nevertheless, this type of data still contains valuable information and can be used if managed in the right way. From the case study it can be concluded that in order to use the data for more advanced analysis a big-data plan is needed at a strategic level in the organization. The plan summarizes the suggested solution for the managerial issues of the big data for the organization. This plan describes how to handle the data, how the analytic models revealing the information should be designed and the tools and organizational capabilities needed to support the people using the information.

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales. / This master’s thesis presents a new approach to urban area detection and segmentation in hyperspectral images. The proposed method relies on a three-step procedure. First, in order to decrease the computational complexity, an informative three-colour composite image, minimizing as much as possible the loss of information of the spectral content, is computed. To this end, a non-linear dimensionality reduction step, based on two complementary but contradictory criteria of good visualization, namely accuracy and contrast, is achieved for the colour display of each hyperspectral image. In order to discriminate between urban and non-urban areas, the second step consists of extracting some complementary and discriminant features on the resulting (three-band) colour hyperspectral image. To attain this goal, we have extracted a set of features relevant to the description of different aspects of urban areas, which are mainly composed of man-made objects with regular or simple geometrical shapes. We have used simple textural features based on grey-levels, gradient magnitude or grey-level co-occurence matrix statistical parameters combined with structural features based on gradient orientation, and straight segment detection. In order to also reduce the computational complexity and to avoid the so-called “curse of dimensionality” when clustering high-dimensional data, we decided, in the final third step, to classify each individual feature (by a simple K-means clustering procedure) and to combine these multiple low-cost and rough image segmentation results with an efficient fusion model of segmentation maps. The experiments reported in this report demonstrate that the proposed segmentation method is efficient in terms of visual evaluation and performs well compared to existing and automatic detection and segmentation methods of urban areas from hyperspectral images.

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Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales. / This master’s thesis presents a new approach to urban area detection and segmentation in hyperspectral images. The proposed method relies on a three-step procedure. First, in order to decrease the computational complexity, an informative three-colour composite image, minimizing as much as possible the loss of information of the spectral content, is computed. To this end, a non-linear dimensionality reduction step, based on two complementary but contradictory criteria of good visualization, namely accuracy and contrast, is achieved for the colour display of each hyperspectral image. In order to discriminate between urban and non-urban areas, the second step consists of extracting some complementary and discriminant features on the resulting (three-band) colour hyperspectral image. To attain this goal, we have extracted a set of features relevant to the description of different aspects of urban areas, which are mainly composed of man-made objects with regular or simple geometrical shapes. We have used simple textural features based on grey-levels, gradient magnitude or grey-level co-occurence matrix statistical parameters combined with structural features based on gradient orientation, and straight segment detection. In order to also reduce the computational complexity and to avoid the so-called “curse of dimensionality” when clustering high-dimensional data, we decided, in the final third step, to classify each individual feature (by a simple K-means clustering procedure) and to combine these multiple low-cost and rough image segmentation results with an efficient fusion model of segmentation maps. The experiments reported in this report demonstrate that the proposed segmentation method is efficient in terms of visual evaluation and performs well compared to existing and automatic detection and segmentation methods of urban areas from hyperspectral images.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-11-12T19:08:26Z No. of bitstreams: 1 ALCIDES LEITE DE AMORIM - DISSERTAÇÃO PPGECA 1990..pdf: 35306341 bytes, checksum: 58ca99353fdbe330c0375e0d09e45b9a (MD5) / Made available in DSpace on 2018-11-12T19:08:26Z (GMT). No. of bitstreams: 1 ALCIDES LEITE DE AMORIM - DISSERTAÇÃO PPGECA 1990..pdf: 35306341 bytes, checksum: 58ca99353fdbe330c0375e0d09e45b9a (MD5) Previous issue date: 1990-12 / O presente trabalho constitui um estudo da região do nordeste brasileiro, objetivando a definição de regiões representadas pelos postos ou estações com características semelhantes em função de conjuntos de variáveis pluviométricas e fluviométricas. Utilizou-se técnicas de classificação automática aplicadas ao conjunto de variáveis que foram obtidas da combinação do período de referência (ano, semestre e trimestre) com os parâmetros (média aritmética, desvio padrão, coeficiente de variação e coeficiente de assimetria) e o valor máximo. Os dados pluviométricos são compostos por quatrocentos postos no intervalo de tempo entre 1337 e 1973, com trinta anos de registros e uma folga de cinco anos, enquanto os fluviométricos de noventa e sete estações com pelo menos oito anos de registros e que tenham seu inicio nas décadas de sessenta ou setenta ou seu término nas décadas de setenta ou oitenta. Foram aplicados os Métodos "Quick Cluster" e "K-Means" (técnicas de . classificação não hierárquicas) nos conjuntos de variáveis pluviométricas e os Métodos de, "Ward", Ligação Simples, Ligação Completa e Centróide (técnicas hierárquicas) nos conjuntos de variáveis fluviométricas. Foi também discutido a aplicabilidade de cada método. Os resultados decorrentes deste trabalho, ilustrados nos mapas, são úteis para o preenchimento de falhas, geração de dados, determinação da curva regional de probabilidade, determinação de um modelo determinístico tipo Chuva-Uazão, etc. / The present thesis constitutes a study of the north-east region of Brasil, with the objective of defining the groupings of raingauge stations and flow measuring stations, that have similar characteristics. Techniques of automatic cIassifiction as applied to a set of variables were utilised herein. These variables were obtained for a combination of reference periods (being a year, semester or trimester) among the parameters of the station data, such as arithmatic mean, standard deviation, coefficient of variation skewness coefficient and the maximum value. The rainfall data for 400 raingauge stations between the years 1937 to 1973 were utilized in the study. Thirty (30) years of data, with a superposition of atleast 5 years between the stations, were utilized for raingauge stations. The data for flow measuring stations, numbering 97, consisted of reliable data over an eight-year period. The Methods of "Quick Cluster" and "K-Means" (which belong to the techniques of non-hierarquic classification) were applied to the set of precipitation variables and the Methods of," U/ard", Simple Linking, Complete Linking and Centroid (which pertain to hierarquical techniques) were applied to the set of flow variables. The applicability of each of these methods is discussed here-in.

The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.

This thesis deals with mining of relevant information from documents and automatic splitting of multiple documents merged together. Moreover, it describes the design and implementation of software for data mining from documents and for automatic splitting of multiple documents. Methods for acquiring textual data from scanned documents, named entity recognition, document clustering, their supportive algorithms and metrics for automatic splitting of documents are described in this thesis. Furthermore, an algorithm of implemented software is explained and tools and techniques used by this software are described. Lastly, the success rate of the implemented software is evaluated. In conclusion, possible extensions and further development of this thesis are discussed at the end.

The work deals with the measurement and management in power. Provides an introduction to the problems of remote meter reading, management, and describes the current situation in the field of modern technologies Smart metering and Smart grids. It also analyzed the issue of collection of networks and data collection from a large number of meters over a wide area. For the purpose of data transmission are described GPRS, PLC, DSL, ... Further, there are given options to streamline communication. This area is used hierarchical aggregation. Using k-means algorithm is a program designed to calculate the number of concentrators and their location in the group of meters. The finished program is written in Java. It has a graphical interface and shows how the calculation is conducted. To verify the results of the optimization program is given simulation model in OPNET Modeler tool. Audited results are described in the conclusion and can deduce that using the optimization program is to streamline communications.