Topic
Knowledge extraction
About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.
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TL;DR: A knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases and can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research.
78 citations
21 Aug 2011
TL;DR: A taxi business intelligence system to explore the massive taxi location traces from different business perspectives with various data mining functions is developed and will help taxi companies to improve their business performances by understanding the behaviors of both drivers and customers.
Abstract: The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract useful business intelligence, which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors, improving customer experiences, and increasing business performances. However, extracting business intelligence from location traces is not a trivial task. Conventional data analytic tools are usually not customized for handling large, complex, dynamic, and distributed nature of location traces. To that end, we develop a taxi business intelligence system to explore the massive taxi location traces from different business perspectives with various data mining functions. Since we implement the system using the real-world taxi GPS data, this demonstration will help taxi companies to improve their business performances by understanding the behaviors of both drivers and customers. In addition, several identified technical challenges also motivate data mining people to develop more sophisticate techniques in the future.
78 citations
01 Jan 2001
TL;DR: This work incorporates two knowledge discovery techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns that is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data.
Abstract: We incorporate two knowledge discovery techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns. This tool is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data. We present two methods for this exploratory analysis and we detail algorithms to effectively explore geo-referenced data. We illustrate the algorithms with real crime data. We demonstrate our approach to a new type of analysis of the spatio-temporal dimensions of records of criminal events. We hope this will lead to new approaches in the exploration of large volumes of spatio-temporal data.
78 citations
TL;DR: The possibility to automate by means of application of self-organisation and other principles more or less the whole data mining process, what is named self- Organising data mining is described.
Abstract: In the article is described the possibility to automate by means of application of self-organisation and other principles more or less the whole data mining process, what we have named self-organising data mining. There are different GMDH-based modelling algorithms implemented - dimensionality reduction, missing value elimination, active neurons, enhanced network synthesis and creation of systems of equations, validation, combining of alternative models - to make knowledge extraction objective, fast and easy-to-use even for large and complex systems.
78 citations
01 Jan 2015
TL;DR: This chapter offers a first exploration of the general potential of Artificial Intelligence Techniques in Human Resource Management and a brief foundation elaborates on the central functionalities of Artificial intelligence Techniques and the central requirements of Human resource Management based on the task-technology fit approach.
Abstract: Artificial Intelligence Techniques and its subset, Computational Intelligence Techniques, are not new to Human Resource Management, and since their introduction, a heterogeneous set of suggestions on how to use Artificial Intelligence and Computational Intelligence in Human Resource Management has accumulated. While such contributions offer detailed insights into specific application possibilities, an overview of the general potential is missing. Therefore, this chapter offers a first exploration of the general potential of Artificial Intelligence Techniques in Human Resource Management . To this end, a brief foundation elaborates on the central functionalities of Artificial Intelligence Techniques and the central requirements of Human Resource Management based on the task-technology fit approach. Based on this, the potential of Artificial Intelligence in Human Resource Management is explored in six selected scenarios (turnover prediction with artificial neural networks , candidate search with knowledge-based search engines, staff rostering with genetic algorithms , HR sentiment analysis with text mining , resume data acquisition with information extraction and employee self-service with interactive voice response ). The insights gained based on the foundation and exploration are discussed and summarized.
78 citations