Data mining techniques for wireless sensor networks: A survey
Recently, data management and processing for wireless sensor networks (WSNs) has
become a topic of active research in several fields of computer science, such as the …
become a topic of active research in several fields of computer science, such as the …
Residual energy-based adaptive data collection approach for periodic sensor networks
Due to its potential applications and the density of the deployed sensors, distributed wireless
sensor networks are one of the highly anticipated key contributors of the big data in the …
sensor networks are one of the highly anticipated key contributors of the big data in the …
Towards a variable size sliding window model for frequent itemset mining over data streams
M Deypir, MH Sadreddini, S Hashemi - Computers & industrial engineering, 2012 - Elsevier
Sliding window is a widely used model for data stream mining due to its emphasis on recent
data and its bounded memory requirement. The main idea behind a transactional sliding …
data and its bounded memory requirement. The main idea behind a transactional sliding …
SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming
W Xiao, J Hu - The Journal of Supercomputing, 2020 - Springer
Finding frequent itemsets in a continuous streaming data is an important data mining task
which is widely used in network monitoring, Internet of Things data analysis and so on. In the …
which is widely used in network monitoring, Internet of Things data analysis and so on. In the …
A sliding window based algorithm for frequent closed itemset mining over data streams
F Nori, M Deypir, MH Sadreddini - Journal of Systems and Software, 2013 - Elsevier
Frequent pattern mining over data streams is an important problem in the context of data
mining and knowledge discovery. Mining frequent closed itemsets within sliding window …
mining and knowledge discovery. Mining frequent closed itemsets within sliding window …
Sliding window based weighted erasable stream pattern mining for stream data applications
U Yun, G Lee - Future Generation Computer Systems, 2016 - Elsevier
As one of the variations in frequent pattern mining, erasable pattern mining discovers
patterns with benefits lower than or equal to a user-specified threshold from a product …
patterns with benefits lower than or equal to a user-specified threshold from a product …
EK-means: A new clustering approach for datasets classification in sensor networks
In wireless sensor networks (WSNs), hundreds or thousands of nodes are deployed in order
to provide high quality monitoring. Nowadays, they constitute one of the most important …
to provide high quality monitoring. Nowadays, they constitute one of the most important …
Rare pattern mining: challenges and future perspectives
Extracting frequent patterns from databases has always been an imperative task for the data
mining community. Literature has endowed plentiful endeavors to this research area with …
mining community. Literature has endowed plentiful endeavors to this research area with …
A sliding window-based approach for mining frequent weighted patterns over data streams
The mining of frequent weighted patterns (FWPs) that considers the different semantic
significance (weight) of items is more suitable for practice than the mining of frequent …
significance (weight) of items is more suitable for practice than the mining of frequent …
SPPC: a new tree structure for mining erasable patterns in data streams
Abstract Discovering Erasable Patterns (EPs) consists of identifying product parts that will
produce a small profit loss if their production is stopped. It is a data mining problem that has …
produce a small profit loss if their production is stopped. It is a data mining problem that has …