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Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions

Shuo Yu, Feng Xia, Yuchen Sun, Tao Tang, Xiaoran Yan, Ivan Lee
2020 IEEE Transactions on Computational Social Systems  
We propose an approach to generate query conditions based on the user's query.  ...  In this work, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way.  ...  Based on the user's query sub-graph, we first preprocess the data and set the search conditions.  ... 
doi:10.1109/tcss.2020.2977958 fatcat:hv47en6a4jczvgzywa4plhqmzu

Contextual information retrieval based on algorithmic information theory and statistical outlier detection

Rafael Martinez, Manuel Cebrian, Francisco de Borja Rodriguez, David Camacho
2008 2008 IEEE Information Theory Workshop  
(outliers), and novel organization of data base structure.  ...  The paper shows how they can be integrated to retrieve information from generic databases using long (text-based) queries. Two important problems are analyzed in the paper.  ...  These browsers are based on a context-based searching over their documents, not just a keyword search/match.  ... 
doi:10.1109/itw.2008.4578672 dblp:conf/itw/MartinezCRC08 fatcat:zxqy3u4rivaihgdvzjyr5wc6lm

A Neighbor Propagation Clustering Algorithm for Intrusion Detection

Zheng Li
2020 Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information  
This paper mainly applies outlier detection algorithm in intrusion detection, and proposes a novel outlier detection algorithm based on neighbor propagation clustering.  ...  The results prove that our algorithm can detect intrusions with a high accuracy.  ...  So far, the following outlier detection algorithms have been introduced to intrusion detection, including frequent pattern-based algorithm [19] , density-based algorithm [20] , and depth-based algorithm  ... 
doi:10.18280/ria.340311 fatcat:eu7pc2lo6jb6heluvkbnnabt4e

Table of Contents

2021 IEEE Transactions on Computational Social Systems  
Wu Detecting Outlier Patterns With Query-Based Artificially Generated Searching Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Zhao 179 A Topic Representation Model for Online Social Networks Based on Hybrid Human-Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcss.2020.3048569 fatcat:exdtxcmui5dalfans2d5evveai

Semantic-based Detection of Segment Outliers and Unusual Events for Wireless Sensor Networks [article]

Lianli Gao and Michael Bruenig and Jane Hunter
2014 arXiv   pre-print
The experimental results show that the SOUE-Detector can efficiently detect segment outliers and unusual events with high levels of precision and recall.  ...  One of the major challenges associated with wireless sensor networks is the quality of the data and more specifically the detection of segment outliers and unusual events.  ...  To date, we have only evaluated our approach on test datasets that have been artificially generated and that contain outliers that are randomly generated.  ... 
arXiv:1411.2188v1 fatcat:qbougd5hivejvn5x5s5mhfz63y

Pattern matching and associative artificial neural networks for water distribution system time series data analysis

S. R. Mounce, R. B. Mounce, T. Jackson, J. Austin, J. B. Boxall
2014 Journal of Hydroinformatics  
A protocol for applying pattern matching to automatically recognise specific waveforms in time series based on their shapes is described together with a system called Advanced Uncertain Reasoning Architecture  ...  AURA is a class of binary neural network that has a number of advantages over standard artificial neural network techniques for condition monitoring including a sound theoretical basis to determine the  ...  The SDE generates a search index based on binary vectors, in a similar fashion to a conventional text search engine.The SDE provides an interactive and intuitive search capability, which is feature driven  ... 
doi:10.2166/hydro.2013.057 fatcat:hqyzoqel5rghvdnlheizy26v44

KAOS: a new automated computational method for the identification of overexpressed genes

Angelo Nuzzo, Giovanni Carapezza, Sebastiano Di Bella, Alfredo Pulvirenti, Antonella Isacchi, Roberta Bosotti
2016 BMC Bioinformatics  
KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background.  ...  Conclusions: The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search).  ...  Angelo Nuzzo is currently affiliated with the Department of Bioengineering, University of Applied Sciences, Vienna, 1190, Austria.  ... 
doi:10.1186/s12859-016-1188-1 pmid:28185541 pmcid:PMC5123341 fatcat:h5oz5n46mva7pcnn5dbmdoovna

Discovering outlying aspects in large datasets

Nguyen Xuan Vinh, Jeffrey Chan, Simone Romano, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Jian Pei
2016 Data mining and knowledge discovery  
Outlying aspects mining can be used to explain any data point of interest, which itself might be an inlier or outlier.  ...  respect to dimensionality and yet being computationally efficient, and (b) how to efficiently search through the exponentially large search space of all possible subspaces.  ...  For nominal or mixed database, frequent pattern based outlier detection methods such as that proposed by He et al. (2005) can offer interpretability, as the outliers are defined as the data transactions  ... 
doi:10.1007/s10618-016-0453-2 fatcat:h2z3zx7nn5cvhfxtmzcg3scdc4

Quantifying consensus of rankings based on q-support patterns

Zhengui Xue, Zhiwei Lin, Hui Wang, Sally McClean
2019 Information Sciences  
A method for detecting outliers in a set of rankings is naturally derived from the proposed consensus quantifying approach.  ...  Existing studies on consensus measurement are insufficient, since they did not evaluate consensus among most rankings or consensus with respect to specific preference patterns.  ...  The queries were pre-generated by showing images and videos of medical conditions to potential users.  ... 
doi:10.1016/j.ins.2019.12.070 fatcat:gt2oluaeajc75kspn33o2txswy


Mudassir Makhdoomi
2017 International Journal of Advanced Research in Computer Science  
The volume of data being produced is increasing at an exponential rate due to our unprecedented capacity to generate, capture and share vast amounts of data.  ...  Everyone is talking about big data, and it is believed that science, business, industry, government, society, etc. will undergo a thorough change with the influence of big data.  ...  Another common problem is to find data patterns in huge datasets that satisfy certain conditions. This type of search is likely to occur repeatedly during data analysis.  ... 
doi:10.26483/ijarcs.v8i7.4032 fatcat:5okpnzi6s5atjmn54cez4vu77a

Comparative Analysis of Outlier Detection Techniques

Kamal Malik, H. Sadawarti, Kalra G. S
2014 International Journal of Computer Applications  
with each outlier detection technique.  ...  Outlier detection and analysis is sometimes known as outlier mining.  ...  LITERATURE SURVEY Outliers are the patterns of the data that do not comply with the general expected behavior.  ... 
doi:10.5120/17026-7318 fatcat:jhhciu6ud5adhgrt423tjcnrtq

Design of Intelligent layer for flexible querying in databases [article]

Mrs. Neelu Nihalani, Dr. Sanjay Silakari, Dr. Mahesh Motwani
2009 arXiv   pre-print
Database systems support only a Boolean query model. A selection query on SQL database returns all those tuples that satisfy the conditions in the query.  ...  Therefore most new-generation database applications demand intelligent information management to enhance efficient interactions between database and the users.  ...  Another important area of research in Data mining is Outlier detection. Zhao, Bao, Sun, and Yu [12] found the existence of a number of empty cells that are ineffective to outlier detection.  ... 
arXiv:0912.2282v1 fatcat:xnc6fdafwzahtcy7oj3wyuj7ai

Guest Editorial: Special Issue on Hybrid Human–Artificial Intelligence for Social Computing

Weishan Zhang, Huansheng Ning, Lu Liu, Qun Jin, Vincenzo Piuri
2021 IEEE Transactions on Computational Social Systems  
Yu et al. have an interesting work on "Detecting outlier patterns with querybased artificially generated searching conditions," which aims at detecting the outlier behaviors in social networks, equipped  ...  with intelligent users' queries.  ...  , University of Science and Technology Beijing; and the Founder and the Principal at the Cybermatics and Cyberspace International Science and Technology Cooperation Base.  ... 
doi:10.1109/tcss.2021.3049702 fatcat:3k2h5lobcvalnorih5u2us73gq

New Implementations of Data Mining in a Plethora of Human Activities [chapter]

Alberto Ochoa, Julio Ponce, Francisco Ornelas, Ruben Jaramillo, Ramon Zatarain, Maria Barron, Claudia Gomez, Jose Martinez, Arturo Elias
2011 Knowledge-Oriented Applications in Data Mining  
Previous research has established that the use of outlier analysis is one of the best techniques for the detection of fraud in general.  ...  Each node has only a query ant, which generates a Forward Ant for attending only one user query, assigning the searched keyword t to the Forward Ant.  ... 
doi:10.5772/13454 fatcat:j6oc3h7dnbhsdlpuowmmjzmdcq

Sensor data quality: a systematic review

Hui Yie Teh, Andreas W. Kempa-Liehr, Kevin I-Kai Wang
2020 Journal of Big Data  
It is similar to an artificial neural network, but unlike most neural networks, HTM can learn time-based patterns in an unlabeled data stream.  ...  Study selection The initial search query (1) returned 13,057 publications. After the process of topic modelling, the refined search query (2) resulted in 6970 publications.  ...  The benchmark should provide datasets complete with all the different types of errors (that is either labelled or injected artificially) and a proper scoring system that uses the appropriate evaluation  ... 
doi:10.1186/s40537-020-0285-1 fatcat:cbl346kh35cqvn6nh7njzvrq5e
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