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Data Mining for the Social Sciences, 2019
Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and signiicant structures, from large amounts of data stored in databases, data warehouses, or other information repositories.
Kimberly Kirkpatrick
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Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and signiicant structures, from large amounts of data stored in databases, data warehouses, or other information repositories.
Kimberly Kirkpatrick
semanticscholar +1 more source
, 2013 The trajectories of a moving object are a powerful summary for its activity related to mobility. As seen in previous chapters, such information can be queried in order to retrieve those trajectories (and the objects that own them) that respond to some given search criteria, for instance following a predefined interesting behavior. However, when massive
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Statistics, Data Mining, and Machine Learning in Astronomy
, 2019Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark ...
Ž. Ivezić +3 more
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Data Mining and Knowledge Discovery in Databases
Advances in Computer and Electrical Engineering, 2019The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods.
A. Azevedo
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2005
Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions,
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Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions,
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FROM DATA MINING TO BEHAVIOR MINING [PDF]
International Journal of Information Technology & Decision Making, 2006 Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of ...
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International Journal of Knowledge Discovery in Bioinformatics, 2009
In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds.
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In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds.
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2013
This chapter introduces the standard formulation for the data input to data mining algorithms that will be assumed throughout this book. It goes on to distinguish between different types of variable and to consider issues relating to the preparation of data prior to use, particularly the presence of missing data values and noise.
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This chapter introduces the standard formulation for the data input to data mining algorithms that will be assumed throughout this book. It goes on to distinguish between different types of variable and to consider issues relating to the preparation of data prior to use, particularly the presence of missing data values and noise.
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Data preprocessing in predictive data mining
Knowledge engineering review (Print), 2019A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset.
Stamatios-Aggelos N. Alexandropoulos +2 more
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Data mining and electroencephalography
Statistical Methods in Medical Research, 2000An overview of data mining (DM) and its application to the analysis of DM and electroencephalography (EEG) is given by: (i) presenting a working definition of DM, (ii) motivating why EEG analysis is a challenging field of application for DM technology and (iii) by reviewing exemplary work on DM applied to EEG analysis. The current status of work on DM
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