Online mining for association rules and collective anomalies in data streams

Author(s):  
Shaaban Abbady ◽  
Cheng-Yuan Ke ◽  
Jennifer Lavergne ◽  
Jian Chen ◽  
Vijay Raghavan ◽  
...  
Author(s):  
Ho Jin Woo ◽  
Se Jung Shin ◽  
Kil Hong Joo ◽  
Won Suk Lee

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.


2018 ◽  
Vol 7 (4.10) ◽  
pp. 436
Author(s):  
R. M.Rani ◽  
M. Pushpalatha

Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making. Currently in wireless sensor networks, various mining techniques are used to discover knowledge on sensor data. Applying mining algorithm in wireless sensor data faces many challenges such as continuous arrival of sensor data, fast and huge data arrival, changes of mining results over time, online mining, data transformation, changing network topology, resource constraints and have emerged into various research problems.  In Wireless Sensor Database, this paper presents a review on various approaches of association rule mining algorithms using various techniques forming sensor association rules generating frequent patterns to find upcoming sensor events or sensor fault detection or to estimate the missing sensor readings.  


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