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2. Electricity Consumption Time Series Profiling: A Data Mining Application in Energy Industry

Author

Liu, Hongyan, Yao, Zhiyuan, Eklund, Tomas, Back, Barbro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Perner, Petra, editor

Published
2012
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6. Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis

Author

Yao, Zhiyuan, Holmbom, Annika H., Eklund, Tomas, Back, Barbro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Perner, Petra, editor

Published
2010
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7. Relevance Ranking of Intensive Care Nursing Narratives

Author

Suominen, Hanna, Pahikkala, Tapio, Hiissa, Marketta, Lehtikunnas, Tuija, Back, Barbro, Karsten, Helena, Salanterä, Sanna, Salakoski, Tapio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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27. Strategic pricing possibilities of grocery retailers : an empirical study

Author

Länsiluoto, Aapo, Back, Barbro, and Vanharanta, Hannu

Subjects
TheoryofComputation_MISCELLANEOUS
Abstract

The right pricing of products is one of the most important issues concerning the development of companies’ financial performance. Prices should be low enough to attract customers and at the same time high enough to cover all the emerged costs and expected profits. This research illustrates how self-organizing maps (SOM) can be used for pricing purposes. We show how changes in a company’s pricing policies would affect the company’s pricing position. The study illustrates clearly that companies have different possibilities to change their pricing positions. The SOM method is new and can be applied in many different ways through different pricing simulations., La tarifación correcta de los productos es una de los problemas más importantes, en relación con el desarrollo del desempeño financiero de las compañías. Los precios deberían ser lo suficientemente bajos para atraer a los clientes, y al mismo tiempo, lo suficientemente altos para cubrir todos los costes emergentes y los beneficios previstos. Esta investigación ilustra cómo los mapas auto-organizadores (SOM en inglés) pueden ser usados para fines de tarifación. Mostramos cómo los cambios en las políticas de tarifación en una empresa, pueden afectar a la posición de tarifación de la misma. El estudio muestra claramente que las empresas tienen diferentes posibilidades para cambiar dichas posiciones. El método SOM es nuevo y puede ser aplicado de muchas maneras mediante varias simulaciones de tarifación.

Published
2007

28. Mining textual contents of financial report

Author

Kloptchenko, Antonina, Magnusson, Camilla, and Back, Barbro

Abstract

The message, stylistic focus, language and readability of financial reports are good indicators of the perspectives and developments of any company. These indicators can guide companies' decision makers to more efficient actions in the dynamic business environment. In this paper, we have studied the language and contents of quarterly financial reports using automated linguistic and text mining methods. We aim at comparing the results from linguistic analysis of quarterly reports by means of collocational networks and the results obtained from text mining analysis of quarterly report by means of the prototype matching. We perform the study on the quarterly reports from three leading companies in the telecommunications sector. Our results are somewhat controversial: some of the reports from the companies have as their closest matches the reports with similar collocational networks and some do not have., El mensaje, el enfoque estilístico, el idioma y facilidad para leer de los reportes financieros son buenos indicadores de las perspectivas y desarrollos de cualquier compañía. Estos indicadores pueden guiar la toma de decisiones de las compañías y dirigirlas hacia decisiones eficientes en el entorno dinámico de los negocios. En este artículo, hemos estudiado el lenguaje y contenidos de reportes financieros usando métodos lingüísticos automatizados. Nuestro objetivo es comparar los resultados del análisis lingüístico en función de las redes online de colocación y los resultados en función de los prototipos que encaje. Realizamos el estudio en los informes de tres empresas líderes en el sector de la telecomunicación. Nuestros resultados son de alguna forma controvertidos: algunos de los informes de las compañías tienen mayor coincidencia los informes con redes de colocación más similares, mientras que otros no.

Published
2004

38. A Weak-orm Expert Evaluation of Customer Profiling Models.

Author

Holmbom, Annika, Eklund, Tomas, and Back, Barbro

Subjects
EXPERT systems, CONSUMER profiling, MARKET basket analysis, MARKET segmentation, CONSUMER behavior, DATA mining
Abstract

In this paper, we evaluate two models that were used for customer profiling: 1) a market basket analysis (MBA) model and 2) a customer segmentation model. The models were based on actual customer purchasing data from a large department store for the period 2007-09. A weak-form evaluation method, consisting of qualitative interviews of experts from the department store, was used. The questions focused on information quality according to the DeLone and McLean framework and were derived from the Doll and Torkzadeh model, i.e., they covered content, accuracy, format, and ease of use aspects. Seven experts from the case company took part in the evaluation process. Before the actual interview, the experts were asked to fill out a questionnaire regarding their background and current access to timely sales information, i.e., how often and how useful the information was that they were receiving at the moment. Later, during an interview, the evaluator discussed the questionnaire with each respondent. Then, the experts were presented with information on their customers' buying behavior based on the results from the two models, i.e., the MBA- and the segmentation model. After each presentation, the experts were asked to respond to fifteen statements and four open-ended questions. All in all, the information gained through the MBA- and segmentation analyses was rated highly (4-5/max 5) by the experts. The experts considered the information gained with help of these models to be valuable and useful for decision making and for making strategic planning for the future. This implies that the models could be of valuable use for managers working within CRM, e.g., planning marketing campaigns, product range planning, service development, planning of store layouts, operative and strategic planning, and for further developing loyalty programs. [ABSTRACT FROM AUTHOR]

Published
2014

43. Relevance Ranking of Intensive Care Nursing Narratives.

Author

Gabrys, Bogdan, Howlett, Robert J., Jain, Lakhmi C., Suominen, Hanna, Pahikkala, Tapio, Hiissa, Marketta, Lehtikunnas, Tuija, Back, Barbro, Karsten, Helena, Salanterä, Sanna, and Salakoski, Tapio

Abstract

Current computer-based patient records provide many capabilities to assist nurses' work in intensive care units, but the possibilities to utilize existing free-text documentation are limited without the appropriate tools. To ease this limitation, we present an adaptation of the Regularized Least-Squares (RLS) algorithm for ranking pieces of nursing notes with respect to their relevance to breathing, blood circulation, and pain. We assessed the ranking results by using Kendall's τb as a measure of association between the output of the RLS algorithm and the desired ranking. The values of τb were 0.62, 0.69, and 0.44 for breathing, blood circulation, and pain, respectively. These values indicate that a machine learning approach can successfully be used to rank nursing notes, and encourage further research on the use of ranking techniques when developing intelligent tools for the utilization of nursing narratives. [ABSTRACT FROM AUTHOR]

Published
2006
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49. Towards Automated Classification of Intensive Care Nursing Narratives.

Author

Hasman, Arie, Haux, Reinhold, van der Lei, Johan, De Clercq, Etienne, Roger-France, Francis, Hiissa, Marketta, Pahikkala, Tapio, Suominen, Hanna, Lehtikunnas, Tuija, Back, Barbro, Karsten, Helena, Salanterä, Sanna, and Salakoski, Tapio

Abstract

Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. In this paper, we addressed two issues related to designing an automated classifier: domain experts' agreement on the content of the classes into which the data are to be classified, and the ability of the machine-learning algorithm to perform the classification on an acceptable level. The data we used were a set of Finnish intensive care nursing narratives. By using Cohen's κ, we assessed the agreement of three nurses on the content of the classes Breathing, Blood Circulation and Pain, and by using the area under ROC curve (AUC), we measured the ability of the Least Squares Support Vector Machine (LS-SVM) algorithm to learn the classification patterns of the nurses. On average, the values of κ were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The LS-SVM algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. Our results indicate that one way to develop electronic patient records could be tools that handle the free text in nursing documentation. [ABSTRACT FROM AUTHOR]

Published
2006

50. Theoretical Considerations of Ethics in Text Mining of Nursing Documents.

Author

Park, Hyeoun-Ae, Murray, Peter, Delaney, Connie, Suominen, Hanna, Lehtikunnas, Tuija, Back, Barbro, Karsten, Helena, Salakoski, Tapio, and Salanterä, Sanna

Abstract

This paper discusses theoretical considerations of ethics in building and using a text mining application in nursing documentation. Nursing documentation is based on the process of gathering information from the patient, setting goals for care, documenting nursing interventions and evaluating delivered nursing care. Privacy-sensitive health care documentation brings specific ethical concerns and difficulties that one needs to be aware of and conform to when developing and using text mining tools in electronic patient records. We discuss how patient confidentiality can be ensured in this domain and how text mining might support nurses to give better and more efficient care for their patients. Our conclusion is that text mining of nursing documents holds the promise of great benefits when the potential risks are taken into consideration. [ABSTRACT FROM AUTHOR]

Published
2006

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