Aalborg Universitet Sentinel Mining Middelfart, Morten
... opposed to absolute data values. In this chapter, the first sentinel mining algorithm is presented along with its implementation in SQL. Although the implementation of sentinel mining is straight forward, compared to what will be presented in the following chapters, we demonstrate that this particul ...
... opposed to absolute data values. In this chapter, the first sentinel mining algorithm is presented along with its implementation in SQL. Although the implementation of sentinel mining is straight forward, compared to what will be presented in the following chapters, we demonstrate that this particul ...
Data Mining - Lyle School of Engineering
... computational model consisting of five parts: – A starting set of individuals, P. – Crossover: technique to combine two parents to create offspring. – Mutation: randomly change an individual. – Fitness: determine the best individuals. – Algorithm which applies the crossover and mutation techniques t ...
... computational model consisting of five parts: – A starting set of individuals, P. – Crossover: technique to combine two parents to create offspring. – Mutation: randomly change an individual. – Fitness: determine the best individuals. – Algorithm which applies the crossover and mutation techniques t ...
Data Mining - Lyle School of Engineering
... computational model consisting of five parts: – A starting set of individuals, P. – Crossover: technique to combine two parents to create offspring. – Mutation: randomly change an individual. – Fitness: determine the best individuals. – Algorithm which applies the crossover and mutation techniques t ...
... computational model consisting of five parts: – A starting set of individuals, P. – Crossover: technique to combine two parents to create offspring. – Mutation: randomly change an individual. – Fitness: determine the best individuals. – Algorithm which applies the crossover and mutation techniques t ...
Fuzzy Miner A Fuzzy System for Solving Pattern - CEUR
... Fuzzy, statistical and structural approaches are valid approaches to the classification problem. The point is that probability (statistical approach) involves crisp set theory and does not allow for an element to be a partial member in a class. Probability is an indicator of the frequency or likelih ...
... Fuzzy, statistical and structural approaches are valid approaches to the classification problem. The point is that probability (statistical approach) involves crisp set theory and does not allow for an element to be a partial member in a class. Probability is an indicator of the frequency or likelih ...
View/Open - Minerva Access
... of observational databases. Among many other types of information (knowledge) that can be discovered in data, patterns that are expressed in terms of features are popular because they can be understood and used directly by people. The recently proposed Emerging Pattern (EP) is one type of such knowl ...
... of observational databases. Among many other types of information (knowledge) that can be discovered in data, patterns that are expressed in terms of features are popular because they can be understood and used directly by people. The recently proposed Emerging Pattern (EP) is one type of such knowl ...
TESI DOCTORAL
... expertos en el diagnóstico del melanoma considerándolos positivos. Palabras clave. Ayuda al diagnóstico, cáncer de melanoma, razonamiento analógico, sistemas colaborativos, clasificación multietiqueta. ...
... expertos en el diagnóstico del melanoma considerándolos positivos. Palabras clave. Ayuda al diagnóstico, cáncer de melanoma, razonamiento analógico, sistemas colaborativos, clasificación multietiqueta. ...
Combining Classifiers with Meta Decision Trees
... Meta decision trees (MDTs) are a novel method for combining multiple classifiers. The difference between meta and ordinary decision trees (ODTs) is that MDT leaves specify which base-level classifier should be used, instead of predicting the class value directly. The attributes used by MDTs are deri ...
... Meta decision trees (MDTs) are a novel method for combining multiple classifiers. The difference between meta and ordinary decision trees (ODTs) is that MDT leaves specify which base-level classifier should be used, instead of predicting the class value directly. The attributes used by MDTs are deri ...
Data Mining Algorithms
... D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 417-427, Tucson, Arizona, May 1997. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data fo ...
... D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 417-427, Tucson, Arizona, May 1997. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data fo ...
Mining Frequent Sequential Patterns From Multiple Databases
... multiple data sources such as multiple E-Commerce (B2C) web sites for comparative, historical and derived analysis, poses the additional challenge of integrating mined patterns from multiple sources during various levels of mining. A few existing work on mining frequent patterns from multiple databa ...
... multiple data sources such as multiple E-Commerce (B2C) web sites for comparative, historical and derived analysis, poses the additional challenge of integrating mined patterns from multiple sources during various levels of mining. A few existing work on mining frequent patterns from multiple databa ...
Spatio-temporal Co-occurrence Pattern Mining in Data Sets with
... co-location pattern. The authors also introduced the TopologyMiner algorithm to discover the co-location patterns. The algorithm discovers frequent co-location patterns in a depth-first manner. The TopologyMiner algorithm divides the search space into a set of partitions, and then in each partition ...
... co-location pattern. The authors also introduced the TopologyMiner algorithm to discover the co-location patterns. The algorithm discovers frequent co-location patterns in a depth-first manner. The TopologyMiner algorithm divides the search space into a set of partitions, and then in each partition ...
Data Mining Using Neural Networks
... and Computer Engineering, RMIT University for accepting me as a doctoral student. Professor Yu’s depth of knowledge, ideas and work discipline has been very inspirational. I would like to express my sincere thanks to Professor Yu for his support, wise suggestions, encouragement and valuable freedom ...
... and Computer Engineering, RMIT University for accepting me as a doctoral student. Professor Yu’s depth of knowledge, ideas and work discipline has been very inspirational. I would like to express my sincere thanks to Professor Yu for his support, wise suggestions, encouragement and valuable freedom ...
W ONTOLOGY BASED SEMANTIC ANONYMISATION OF MICRODATA Sergio Martínez Lluís
... The exploitation of microdata compiled by statistical agencies is of great interest for the data mining community. However, such data often include sensitive information that can be directly or indirectly related to individuals. Hence, an appropriate anonymisation process is needed to minimise the r ...
... The exploitation of microdata compiled by statistical agencies is of great interest for the data mining community. However, such data often include sensitive information that can be directly or indirectly related to individuals. Hence, an appropriate anonymisation process is needed to minimise the r ...
Optimal Candidate Generation in Spatial Co
... are ordered according to their timestamps, which is irrelevant to data mining. The notion of proximity between data objects is also absent in traditional association rules mining. In co-location mining applications, the natural transactions are absent. A simple co-location dataset example is present ...
... are ordered according to their timestamps, which is irrelevant to data mining. The notion of proximity between data objects is also absent in traditional association rules mining. In co-location mining applications, the natural transactions are absent. A simple co-location dataset example is present ...
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Why Data Mining - start [kondor.etf.rs]
... and protocols – Sending and receiving the not-understood message – Correct implementation of communicative acts defined in the specification – Freedom to use communicative acts with other names, not defined in the specification – Obligation of correctly generating messages in the transport form – La ...
... and protocols – Sending and receiving the not-understood message – Correct implementation of communicative acts defined in the specification – Freedom to use communicative acts with other names, not defined in the specification – Obligation of correctly generating messages in the transport form – La ...
Data Warehousing and Mining
... Classification Tools: Most commonly used in data mining. Classification tools attempt to distinguish different classes of objects or actions. For example, in a case of a credit card transaction, these tools could classify it as one or the other. This will save the credit card company a considerable ...
... Classification Tools: Most commonly used in data mining. Classification tools attempt to distinguish different classes of objects or actions. For example, in a case of a credit card transaction, these tools could classify it as one or the other. This will save the credit card company a considerable ...
Progressive Skyline Computation in Database Systems
... taoyf@cs.cityu.edu.hk; G. Fu, JP Morgan Chase, 277 Park Avenue, New York, NY 10172-0002; email: gregory.c.fu@jpmchase.com; B. Seeger, Department of Mathematics and Computer Science, Philipps University, Hans-Meerwein-Strasse, Marburg, Germany 35032; email: seeger@mathematik.unimarburg.de. Permission ...
... taoyf@cs.cityu.edu.hk; G. Fu, JP Morgan Chase, 277 Park Avenue, New York, NY 10172-0002; email: gregory.c.fu@jpmchase.com; B. Seeger, Department of Mathematics and Computer Science, Philipps University, Hans-Meerwein-Strasse, Marburg, Germany 35032; email: seeger@mathematik.unimarburg.de. Permission ...
- University of Huddersfield Repository
... In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy assoc ...
... In this thesis, several contributions were made. Some new techniques were proposed, i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree), maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’ Generalization Sequences), and fuzzy assoc ...