Online mining for association rules and collective anomalies in data streams

2017 ◽  
Author(s):  
Shaaban Abbady ◽  
Cheng-Yuan Ke ◽  
Jennifer Lavergne ◽  
Jian Chen ◽  
Vijay Raghavan ◽  
...  
Keyword(s):  
Association Rules ◽  
Data Streams ◽  
Online Mining

Finding Context Association Rules over Sensor-Actuator Data Streams

2014 ◽  
Author(s):  
Ho Jin Woo ◽  
Se Jung Shin ◽  
Kil Hong Joo ◽  
Won Suk Lee
Keyword(s):  
Association Rules ◽  
Data Streams ◽  
Sensor Actuator

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

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.

scholarly journals Discovery of Knowledge Using Association Rules in Wireless Sensor Epocs-a Survey

2018 ◽  
Vol 7 (4.10) ◽  
pp. 436
Author(s):  
R. M.Rani ◽  
M. Pushpalatha
Keyword(s):  
Association Rules ◽  
Resource Constraints ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Rule Mining ◽  
Huge Data ◽  
Research Problems ◽  
Sensor Fault Detection ◽  
Online Mining ◽  
Mining Algorithms

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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