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Data Mining Techniques for Wireless Sensor Networks: A Survey

Azhar Mahmood, Ke Shi, Shaheen Khatoon, Mi Xiao
2013 International Journal of Distributed Sensor Networks  
Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.  ...  , and the data mining.  ...  For example, frequent pattern mining over data stream is presented in [31, 32] . A survey on clustering algorithm for WSNs is presented in [33, 34] .  ... 
doi:10.1155/2013/406316 fatcat:wdrq5vffqbcylcdqvqinx4vjui

A Review of Window Query Processing for Data Streams

Hyeon Gyu Kim, Myoung Ho Kim
2013 Journal of Computing Science and Engineering  
Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context.  ...  This paper surveys the previous efforts to resolve these issues in processing data streams.  ...  Many interesting issues related to data stream processing could not be included in this survey, for example: • Classification and clustering • Frequent pattern mining • Synopsis structures, such as reservoir  ... 
doi:10.5626/jcse.2013.7.4.220 fatcat:5nijl3hqgffnrmk3utdtelc6nm

Mining Frequent Itemsets in a Stream

Toon Calders, Nele Dexters, Bart Goethals
2007 Seventh IEEE International Conference on Data Mining (ICDM 2007)  
Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets.  ...  Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items.  ...  Next to mining the frequent itemsets in one stream, further extension to, for instance, distributed streams [22] have been proposed, or even to mining graph patterns over streams [1] .  ... 
doi:10.1109/icdm.2007.66 dblp:conf/icdm/CaldersDG07 fatcat:msmzybclmfh6fndau5op32th5a

Mining frequent itemsets in a stream

Toon Calders, Nele Dexters, Joris J.M. Gillis, Bart Goethals
2014 Information Systems  
Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets.  ...  Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items.  ...  Next to mining the frequent itemsets in one stream, further extension to, for instance, distributed streams [22] have been proposed, or even to mining graph patterns over streams [1] .  ... 
doi:10.1016/j.is.2012.01.005 fatcat:byuzk5e4s5fubesajdlwxak54u

Mining association rules for the quality improvement of the production process

Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget
2013 Expert systems with applications  
Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases.  ...  The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process.  ...  As a result, several algorithms have been developed over time (a review of frequent pattern mining algorithms is described in (Tiwari, Gupta, & Agrawal, 2010) ).  ... 
doi:10.1016/j.eswa.2012.08.039 fatcat:az5ddo6ynvauhgjhgohkpln7wq

A comprehensive survey of anomaly detection techniques for high dimensional big data

Srikanth Thudumu, Philip Branch, Jiong Jin, Jugdutt (Jack) Singh
2020 Journal of Big Data  
data to be mined for common patterns.  ...  Leung and Jiang [36] reported a solution which utilizes MapReduce to mine uncertain big data for frequent patterns satisfying user-specified anti-monotonic restrictions.  ...  Authors' contributions ST conducted the systematic literature review and examined various techniques related to the problems of anomaly detection in high-dimensional big data.  ... 
doi:10.1186/s40537-020-00320-x fatcat:nrx7fnuzbvf65edoisv65by4s4

Applications of Data Mining Techniques for Vehicular Ad hoc Networks [article]

Mohammed AL Zamil, Samer Samarah
2018 arXiv   pre-print
In this paper, we have proposed taxonomy of data mining techniques that have been applied in this domain in addition to a classification of these techniques.  ...  The proposed taxonomy covers elementary data mining techniques such as: preprocessing, outlier detection, clustering, and classification of data.  ...  Several surveys and reviews have been examined data mining techniques on different domains of knowledge such as medical domain [18] , data stream analysis for extracting frequent patterns [19, 20] ,  ... 
arXiv:1807.02564v1 fatcat:6yqvnp3stvhnpbehi4m7t6ghfm

Asynchronous scheduling of redundant disk arrays

P. Sanders
2003 IEEE transactions on computers  
Sorting the requests by track number is a good approach for a single disk if we do not care about large delays for some requests.  ...  A particular emphasis is on randomized techniques for coping with irregular access patterns, massive data sets, and memory hierarchies.  ... 
doi:10.1109/tc.2003.1228512 fatcat:dsyxq2qlyfhltamz5djhuxhrma

Machine Learning [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
ML owes its burgeoning adoption to its ability to characterize underlying relationships within large arrays of data in ways that solve problems in big data analytics, behavioral pattern recognition, and  ...  For example, ML systems can be trained on automatic speech recognition systems (such as iPhone's Siri) to convert acoustic information in a sequence of speech data into semantic structure expressed in  ...  Scaling Up for High-Dimensional Data and High-Speed Data Streams Designing classifiers that can handle very high-dimensional features extracted through high-speed data streams is challenging.  ... 
doi:10.1007/978-1-4302-5990-9_1 fatcat:5hwjpdcxb5ctlavhtw3iql2oei

Periodic subgraph mining in dynamic networks

Mayank Lahiri, Tanya Y. Berger-Wolf
2009 Knowledge and Information Systems  
To identify such regular behavior in streams of dynamic interaction data, we propose a new mining problem of finding a minimal set of periodically recurring subgraphs to capture all periodic behavior in  ...  The algorithm makes a single pass over the data and is also capable of accommodating imperfect periodicity.  ...  We are grateful to Dan Rubenstein, Ilya Fischhoff, and Siva Sundaresan of the Department of Ecology and Evolutionary Biology at Princeton University for sharing the Plains zebra data.  ... 
doi:10.1007/s10115-009-0253-8 fatcat:axqalm7uqjcfxlbbzvm5prv4my

An Introduction to Sensor Data Analytics [chapter]

Charu C. Aggarwal
2012 Managing and Mining Sensor Data  
This requires the development of trajectory mining techniques, which can mine the GPS data for interesting social patterns.  ...  future position, and (iii) mining, extracting interesting patterns from spatiotemporal data.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1007/978-1-4614-6309-2_1 fatcat:pfbx566yfzgqpnjcuzonmxr23q

Robust and timely communication over highly dynamic sensor networks

Tian He, Brian M. Blum, Qing Cao, John A. Stankovic, Sang H. Son, Tarek F. Abdelzaher
2007 Real-time systems  
Based on this concept and the knowledge of the node positions, we introduce Implicit Geographic Forwarding (IGF), a new protocol for highly dynamic sensor networks that is altogether statefree.  ...  Traditional state-based protocols, designed for static and/or low-mobility networks, suffer excessive delay in updating their routing or neighborhood tables, leading to severe packet loss and communication  ...  With the same speed, a smaller range leads to a high mobility.  ... 
doi:10.1007/s11241-007-9025-2 fatcat:yussflm5v5dmhe6g5yze4s7zhu

Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things

Amany Safia, Zaher Aghbari, Ibrahim Kamel
2017 Information  
Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.  ...  Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon.  ...  Due to the characteristics of these data streams (infinite, continuous and high rate), a practical solution to detect phenomena from data streams is to divide the time dimension into time windows, w.  ... 
doi:10.3390/info8040123 fatcat:v3blmjllsvfdbad3djybddzx24

Review of Medical Disease Symptoms Prediction Using Data Mining Technique

Rahul Deo Sah, Dr. Jitendra Sheetalani
2017 IOSR Journal of Computer Engineering  
Now a day's data mining technique used in the field of medical diagnose of critical diesis and clinical data. The prediction of mining technique is major issue.  ...  For the enhancement of mining technique used various approach such as fuzzy logic, feature optimization and machine learning based classification technique. in this classification proceed based on classifier  ...  In addition, concept-drift occurs in the stream when the underlying concepts of the stream change over time.  ... 
doi:10.9790/0661-1903015970 fatcat:6mb6vfdbzjbofck6qyvo6ozlfq

Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

Yun Chi, Haixun Wang, Philip S. Yu, Richard R. Muntz
2006 Knowledge and Information Systems  
This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space.  ...  Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements  ...  Acknowledgements We thank the anonymous reviewers for their invaluable comments and suggestions. We thank Professor Mohammed J. Zaki for providing the Charm source code.  ... 
doi:10.1007/s10115-006-0003-0 fatcat:ct2xwo3dv5bdvbx5qqtetn22fe
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