Adhikari, Animesh.
Advances in Knowledge Discovery in Databases [electronic resource] / by Animesh Adhikari, Jhimli Adhikari. - XV, 370 p. 136 illus. online resource. - Intelligent Systems Reference Library, 79 1868-4394 ; . - Intelligent Systems Reference Library, 79 .
Introduction -- Synthesizing conditional patterns in a database -- Synthesizing arbitrary Boolean expressions induced by frequent itemsets -- Measuring association among items in a database -- Mining association rules induced by item and quantity purchased -- Mining patterns different related databases -- Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources -- Clustering items in time-stamped databases -- Synthesizing some extreme association rules from multiple databases -- Clustering local frequency items in multiple data sources -- Mining patterns of select items in different data sources -- Mining calendar-based periodic patterns in time-stamped data -- Measuring influence of an item in time-stamped databases -- Clustering multiple databases induced by local patterns -- Enhancing quality of patterns in multiple related databases -- Concluding remarks.
This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis. .
9783319132129
10.1007/978-3-319-13212-9 doi
Engineering.
Data mining.
Artificial intelligence.
Computational intelligence.
Engineering.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Artificial Intelligence (incl. Robotics).
Q342
006.3
Advances in Knowledge Discovery in Databases [electronic resource] / by Animesh Adhikari, Jhimli Adhikari. - XV, 370 p. 136 illus. online resource. - Intelligent Systems Reference Library, 79 1868-4394 ; . - Intelligent Systems Reference Library, 79 .
Introduction -- Synthesizing conditional patterns in a database -- Synthesizing arbitrary Boolean expressions induced by frequent itemsets -- Measuring association among items in a database -- Mining association rules induced by item and quantity purchased -- Mining patterns different related databases -- Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources -- Clustering items in time-stamped databases -- Synthesizing some extreme association rules from multiple databases -- Clustering local frequency items in multiple data sources -- Mining patterns of select items in different data sources -- Mining calendar-based periodic patterns in time-stamped data -- Measuring influence of an item in time-stamped databases -- Clustering multiple databases induced by local patterns -- Enhancing quality of patterns in multiple related databases -- Concluding remarks.
This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis. .
9783319132129
10.1007/978-3-319-13212-9 doi
Engineering.
Data mining.
Artificial intelligence.
Computational intelligence.
Engineering.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Artificial Intelligence (incl. Robotics).
Q342
006.3