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Pattern mining for routine behaviour discovery in pervasive healthcare environments

Raza Ali, Mohamed ElHelw, Louis Atallah, Benny Lo, Guang-Zhong Yang
2008 2008 International Conference on Technology and Applications in Biomedicine  
A novel pattern mining algorithm is applied to the pervasive sensing data to obtain a concise, variable-resolution representation of frequent activity patterns over time.  ...  The identification of such frequent patterns enables the observation of the inherent structure present in a patient's daily activity for analyzing routine behaviour and its deviations.  ...  FP-Stream [8] is an example of a frequent pattern mining algorithm that incorporates temporal information by mining a data stream at different granularities.  ... 
doi:10.1109/itab.2008.4570576 fatcat:vg3l4q6fk5fpjhlk55mzyu3ceq

A Novel Frequent Pattern Mining Technique for Prediction of User Behavior on Web Stream Data

Pandluri Dhanalakshmi
2019 Ingénierie des Systèmes d'Information  
Experimental results proved that the present filtered based frequent pattern mining model efficiently predicts the user navigation patterns with high accuracy and less runtime.  ...  In this paper, a novel filter based user navigation pattern mining model is designed and implemented on the large online steaming databases.  ...  Predicting the future navigation pattern mining using historical behaviour and user patterns, and generate the user correlated patterns using min threshold in 1 or more sessions.  ... 
doi:10.18280/isi.240107 fatcat:7zu5deobonel7kwkizl2p3ygba

Mining Closed Regular Patterns in Data Streams

Sreedevi M, Reddy L.S.S
2013 International Journal of Computer Science & Information Technology (IJCSIT)  
To our knowledge no method has been proposed to mine closed regular patterns in data streams.  ...  Mining regular patterns in data streams is an emerging research area and also a challenging problem in present days because in Data streams new data comes continuously with varying rates.  ...  Mining data stream requires fast, real time processing in order to keep up with high speed data arrival and results must be attracted with in short response time.  ... 
doi:10.5121/ijcsit.2013.5114 fatcat:bof3rdxifvekto4mn6rsk6jk7y

Analysis of Users Behaviour in Structured E-Commerce Websites using Web Usage Mining Techniques

Nikhat Fatima
2019 International Journal for Research in Applied Science and Engineering Technology  
Keywords: E-commerce, behaviour, website, pattern, model checking techniques, user, linear temporal model, data mining technique I.  ...  The web data mining techniques are very much useful for find outing patterns in log files.  ...  They have computed the patterns called mining significant. With this techniques prediction of click stream is carried out.  ... 
doi:10.22214/ijraset.2019.8036 fatcat:ppam2sgmhvcxdldkve4ulyixf4

Mining frequent patterns from network flows for monitoring network

Xin Li, Zhi-Hong Deng
2010 Expert systems with applications  
of algorithms that contains vertical re-mining algorithm, multi-pattern re-mining algorithm, fast multi-pattern capturing algorithm and fast multi-pattern capturing supplement algorithm to deal with a  ...  So in the paper we aim to use the technique of frequent pattern mining to find out these events.  ...  Stream mining Recently, algorithms of mining frequent patterns in stream data become a hot topic in research area.  ... 
doi:10.1016/j.eswa.2010.06.012 fatcat:gkiwovlo6fhapn3oowpyxhg7nq

Survey anomaly detection in network using big data analytics

Y.S. Kalai Vani, Krishnamurthy
2017 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)  
data mining and machine learning, deep learning, and Big Data analytics in network intrusion detection.  ...  This paper presents methods and subsequent evaluation criteria for network intrusion detection, stream data characteristics and stream processing systems, feature extraction and data reduction, conventional  ...  Stream data mining involves dynamic changes and efficient discovery of general patterns within the stream data.  ... 
doi:10.1109/icecds.2017.8390083 fatcat:6gij7behqjhezefwcxoflowtqe

Self-configuration from a Machine-Learning Perspective [article]

Wolfgang Konen
2011 arXiv   pre-print
of interesting patterns) or both.  ...  Another aspect is the self-configuration by pattern detection or feature construction.  ...  patterns in data. we know that data mining is tightly connected with pattern finding.  ... 
arXiv:1105.1951v2 fatcat:4vhmi5jh5zbcriydrevbdre7te

Application of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection

Limin Shen, Zhongkui Sun, Lei Chen, Jiayin Feng, Guoqiang Wang
2021 Mathematical Problems in Engineering  
Aiming at the high-dimensional characteristics of data in the intrusion detection system, but the traditional frequent-pattern-based outlier mining algorithm has the problems of difficulty in obtaining  ...  complete frequent patterns and high time complexity, the outlier set is further analysed to get the attack pattern of intrusion detection.  ...  Zhou [16] proposed a new metric called weighted frequent pattern outlier factor for categorical data streams based on FindFPOF and proposed a fast outlier detection method for high-dimensional categorical  ... 
doi:10.1155/2021/9234084 fatcat:w6bbpxejkjf3tmi47iuzmf65ji

An integrated multi-sensing framework for pervasive healthcare monitoring

M. ElHelw, J Pansiot, D. McIlwraith, R. Ali, B. Lo, L. Atallah
2009 Proceedings of the 3d International ICST Conference on Pervasive Computing Technologies for Healthcare  
However, developing functional pervasive systems is a complex task that entails the creation of appropriate sensing platforms, integration of versatile technologies for data stream management and development  ...  Pattern Mining/or Routine Behaviour Discovery Complementary to a model based approach for behaviour profiling is to discover patterns of activities using pattern mining algorithms.  ...  These combinations are discovered by the proposed pattern mining algorithm.  ... 
doi:10.4108/icst.pervasivehealth2009.6038 dblp:conf/ph/ElHelwPMALA09 fatcat:rubbrs4esjemzmcgy5wwuzufii

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

R. M.Rani, M. Pushpalatha
2018 International Journal of Engineering & Technology  
Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making.  ...  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  ...  This approach is only suitable for offline mining on large transactional data base and not suitable for data stream mining.  ... 
doi:10.14419/ijet.v7i4.10.21035 fatcat:34lf5ni3lra67kujp5xz5digfm

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

Muhammad Fahim, Thar Baker, Asad Khattak, Babar Shah, Saiqa Aleem, Francis Chow
2018 Sensors  
This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone.  ...  Micro-context recognition of sedentary behaviour using smartphone. In Abstract: Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting.  ...  Discussion Context mining of sedentary behaviour and visualization of individual patterns may promote self-awareness to reduce it.  ... 
doi:10.3390/s18030874 pmid:29543763 pmcid:PMC5877307 fatcat:5wp7ojxjwvfynar2ec7avjup4i

Sampling for Sequential Pattern Mining: From Static Databases to Data Streams

Chedy Raissi, Pascal Poncelet
2007 Seventh IEEE International Conference on Data Mining (ICDM 2007)  
Moreover, we extend these sampling analysis and present an algorithm based on reservoir sampling to cope with sequential pattern mining over data streams.  ...  Sequential pattern mining is an active field in the domain of knowledge discovery.  ...  We provided compelling evidence that it is possible to obtain accurate and fast results for sequential pattern mining using small samples.  ... 
doi:10.1109/icdm.2007.82 dblp:conf/icdm/RaissiP07 fatcat:5asvxvhzwvfm5mprfspqesoqom

A MapReduce Architecture for Web Site User Behaviour Monitoring in Real Time

Bill Karakostas, Babis Theodoulidis
2013 Proceedings of the 2nd International Conference on Data Technologies and Applications  
Monitoring the behaviour of large numbers of web site users in real time poses significant performance challenges, due to the decentralised location and volume of generated data.  ...  Distributed and Parallel Architectures for User Behaviour Mining Since the early research in web mining of user data, it became apparent that an ideal mining method should provide frequent patterns in  ...  One of the objectives of user behaviour mining is to discover frequent patterns of web site usage concentrated over a period of time, or very long sequential patterns. (Maseglia et al, 2002) .  ... 
doi:10.5220/0004332600450052 dblp:conf/data/KarakostasT13 fatcat:ftg3slbrgfgzdcxjo3atdjz72e

Developing Data Mining Techniques for Intruder Detection in Network Traffic

Amar Agrawal, Sabah Mohammed, Jinan Fiaidhi
2016 International Journal of Security and Its Applications  
SVM has been used, since it's the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior.  ...  detection, State transition analysis, Pattern matching and Data mining techniques.  ...  Our focus in this paper is the use of Data Mining techniques in IDS. Data Mining is the technique of finding patterns in large data sets.  ... 
doi:10.14257/ijsia.2016.10.8.29 fatcat:2jangjn5trdrvngb3rh65gycx4


Roberto Grossi, Fabrizio Sebastiani, Fabrizio Silvestri
2013 Journal of Discrete Algorithms  
The paper "Fast q-gram mining on SLP compressed strings", by Keisuke Goto, Hideo Bannai, Shunsuke Inenaga, and Masayuki Takeda, presents simple and efficient algorithms for calculating q-gram frequencies  ...  ., their capability to predict future user behaviour) from their diagnostic value (i.e., their capability to explain past user behaviour).  ... 
doi:10.1016/j.jda.2012.12.008 fatcat:4p2j2ob3ujhcnikomgf2vjeh24
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