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Discovering pan-correlation patterns from time course data sets by efficient mining algorithms

Qian Liu, Shameek Ghosh, Jinyan Li, Limsoon Wong, Kotagiri Ramamohanarao
2018 Computing  
Evaluations on synthetic time course data sets, and yeast cell cycle gene expression data sets indicate that: (i) the algorithm has linear time increment in terms of increasing number of variables; (ii  ...  We prove a correspondence between positive correlation patterns and sequential patterns, and present an efficient single-scan algorithm for mining the correlations.  ...  algorithm for mining all significant pan-correlation patterns from time-course data.  ... 
doi:10.1007/s00607-018-0606-9 fatcat:u7m4mqzabrd7bprcb6564mop34

Efficient Mining of Pan-Correlation Patterns from Time Course Data [chapter]

Qian Liu, Jinyan Li, Limsoon Wong, Kotagiri Ramamohanarao
2016 Lecture Notes in Computer Science  
This "pan-correlation" mining algorithm is evaluated on synthetic time course data sets, as well as on yeast cell cycle gene expression data sets.  ...  There are different types of correlation patterns between the variables of a time course data set, such as positive correlations, negative correlations, time-lagged correlations, and those correlations  ...  Our pan-correlation mining algorithm is tested on synthetic time course data sets and four microarray gene expression time course data sets.  ... 
doi:10.1007/978-3-319-49586-6_16 fatcat:wm6oicipoje7dnfjgpd332evh4

Guest editorial: special issue on advanced data mining and applications

Quan Z. Sheng, Yongrui Qin
2018 Computing  
The last paper by Liu et al. entitled "Discovering Pan-correlation Patterns from Time Course Data Sets by Efficient Mining Algorithms" focuses on the efficient and effective discovery of correlation patterns  ...  in time-course data.  ... 
doi:10.1007/s00607-018-0607-8 fatcat:g5rmcbhwuvbizdlfmnpnghiali

An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

Tao Pan
2021 International Journal of Emerging Technologies in Learning (iJET)  
This paper firstly relies on the Apriori algorithm to mine the hidden correlations between the physical fitness indices from the PE data on college students, and identify the indices closely associated  ...  Then, the Apriori algorithm was improved to reduce the time complexity of association rule mining.  ...  Correlation Analysis Based on Improved Apriori Algorithm Despite its good mining effect, the Apriori algorithm has a low efficiency.  ... 
doi:10.3991/ijet.v16i09.22747 fatcat:i73aww77vfhcni4nu7h2cc3vci

Discovering patterns in traffic sensor data

Farnoush Banaei-Kashani, Cyrus Shahabi, Bei Pan
2011 Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoStreaming - IWGS '11  
First, the set of road segments can indeed be partitioned into a set of distinct subpartitions with similar traffic flows, and there is a limited number of signature traffic patterns/labels each of which  ...  Traffic sensors (installed under the road pavement) are used to measure real-time traffic flows through road segments.  ...  ACKNOWLEDGMENTS This research has been funded in part by NSF grants CNS-0831505 (CyberTrust) and IS-1115153, the USC Integrated Media Systems Center (IMSC), and unrestricted cash and equipment gifts from  ... 
doi:10.1145/2064959.2064963 dblp:conf/gis/KashaniSP11 fatcat:7thstvoy5bef7oonix7hqggflq

Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Sylvanus A. Ehikioya, Jinbo Zeng
2021 Engineering Reports  
A successful business intelligence solution can help organizations improve the quality and speed of their decision-making processes by analyzing the consolidated information collected from their websites  ...  Presenting items and frequent item sets by vertices and hyperedges respectively, the Hypergraph partition algorithm is able to discover frequent items sets from a hypergraph efficiently in a large number  ...  TA B L E 3 Support of 1 -Sequences The main purpose of mining sequential patterns is to find popular patterns of events in time series from a database which contains sets of time ordered events.  ... 
doi:10.1002/eng2.12411 fatcat:yp57yt6jkvgv7h6oz6vvzxk4fm

Analyzing PACS Usage Patterns by Means of Process Mining: Steps Toward a More Detailed Workflow Analysis in Radiology

Daniel Forsberg, Beverly Rosipko, Jeffrey L. Sunshine
2015 Journal of digital imaging  
Event logs from 1 week of data, corresponding to 567 cases of single-view chest radiographs read by 14 radiologists, were analyzed.  ...  Future research will focus on metrics to describe derived interaction processes in order to investigate if one set of interaction patterns can be considered as more efficient than another set when reading  ...  With the aid of process mining, we may extend the performance analysis of the interaction patterns to not only include the transit time from start to finish of an interaction process but also to include  ... 
doi:10.1007/s10278-015-9824-2 pmid:26353749 pmcid:PMC4722035 fatcat:33pfwh2i3vfszixadofuiml3le

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

Charles D. Stolper, Adam Perer, David Gotz
2014 IEEE Transactions on Visualization and Computer Graphics  
analytics paradigm by clinical researchers analyzing electronic medical records.  ...  Partial results from the progressive analytics enhance the scatterplot, list, and tree visualizations without interfering with users' cognitive workflow.  ...  In addition, we wish to thank Fei Wang for sharing his expertise in frequent sequence mining. Finally, we wish to thank the VAST reviewers for their valuable feedback.  ... 
doi:10.1109/tvcg.2014.2346574 pmid:26356879 fatcat:qh66f2m2avdpveocrdqpak6bgq

The Event Tunnel: Interactive Visualization of Complex Event Streams for Business Process Pattern Analysis

Martin Suntinger, Hannes Obweger, Josef Schiefer, M. Eduard Groller
2008 2008 IEEE Pacific Visualization Symposium  
This facilitates users to discover root causes and causal dependencies of event patterns.  ...  We demonstrate our approach with use cases from the fraud management and logistics domain.  ...  This reference pattern can be used by event-based systems for discovering similar cases.  ... 
doi:10.1109/pacificvis.2008.4475466 dblp:conf/apvis/SuntingerOSG08 fatcat:vwfbuvkeffdunnshmxmvukit34

Integrated analysis of gene expression by Association Rules Discovery

Pedro Carmona-Saez, Monica Chagoyen, Andres Rodriguez, Oswaldo Trelles, Jose M Carazo, Alberto Pascual-Montano
2006 BMC Bioinformatics  
The approach integrates gene annotations and expression data to discover intrinsic associations among both data sources based on co-occurrence patterns.  ...  In this study we present a method for the integrative analysis of microarray data based on the Association Rules Discovery data mining technique.  ...  P.C.S. is the recipient of a fellowship from Comunidad de Madrid (CAM). A.P.M. acknowledges support by the Spanish Ramon y Cajal Program. We thank Dr. Carlos O. Sorzano his valuable comments.  ... 
doi:10.1186/1471-2105-7-54 pmid:16464256 pmcid:PMC1386712 fatcat:eukxnzloozdz3pr7ibtyk4sa2a

Application of data mining in a maintenance system for failure prediction [chapter]

P Bastos, I Lopes, L Pires
2013 Safety, Reliability and Risk Analysis  
Rapid Miner is used to apply different data mining prediction algorithms to maintenance data and compare their accuracy in the discovery of patterns and predictions.  ...  Data mining presents an opportunity to increase significantly the rate at which the volume of data can be turned into useful information.  ...  ACKNOWLEDGEMENTS This work was financed with FEDER Funds by Programa Operacional Fatores de Competitividade-COMPETE and by National Funds by FCT-Fundação para a Ciência e Tecnologia, Project: FCOMP-01-  ... 
doi:10.1201/b15938-138 fatcat:tewanlll45dk3pmmbpjvbll4gy

Task Sensitive Feature Exploration and Learning for Multitask Graph Classification

Shirui Pan, Jia Wu, Xingquan Zhu, Guodong Long, Chengqi Zhang
2017 IEEE Transactions on Cybernetics  
all tasks, by a subset of tasks, or by only one specific task, respectively.  ...  To date, all existing MTL methods are designed for tasks with feature-vector represented instances, but cannot be applied to structured data, such as graphs.  ...  This work was partially supported by the Australian Research Council Discovery Projects under Grant Nos. DP140100545 and DP140102206.  ... 
doi:10.1109/tcyb.2016.2526058 pmid:26978839 fatcat:if4oww4qczdetieav5oxoud3wq

Computational strategies for genome-based natural product discovery and engineering in fungi

Theo A.J. van der Lee, Marnix H. Medema
2016 Fungal Genetics and Biology  
Particular attention will be given to novel algorithms to identify unusual classes of BGCs, as well as integrative pan-genomic approaches that use a combination of genomic and metabolomic data for parallelized  ...  Pan-genomic pattern-based genome mining can be used to connect BGCs to molecules by logically excluding all other possible BGC-molecule connections, by means of a matching algorithm (note the change in  ...  The addition of transcriptome data and the gathering of metabolome/transcriptome data across multiple conditions or time points would enhance such a pattern-based strategy, as the compounds could be correlated  ... 
doi:10.1016/j.fgb.2016.01.006 pmid:26775250 fatcat:3sxc2uxhd5auniq6ea6ovrklna

Customer Classification Of Discrete Customer Assets Data And Re-Ranking Of Classified Data

N. Zahira Jahan, T. Sasitharan null
2020 International Journal of Computer Techniques  
The project "CUSTOMER CLASSIFICATION OF DISCRETE CUSTOMER ASSETS DATA AND RE-RANKING OF CLASSIFIED DATA".  ...  of outlier customer data of a company.  ...  Another example, data mining is a method which discover or investigates diverse agencies of correlated data inside the database which similarly can be used for predictive evaluation in near future.  ... 
doi:10.29126/23942231/ijct-v7i2p3 fatcat:fghqatk6grcchfokpc2vodhb2q

Machine learning: from radiomics to discovery and routine

G. Langs, S. Röhrich, J. Hofmanninger, F. Prayer, J. Pan, C. Herold, H. Prosch
2018 Der Radiologe (Berlin. Print)  
Machine learning in radiology aims at training computers to recognize patterns in medical images and to support diagnosis by linking these patterns to clinical parameters such as treatment or outcome.  ...  These methods enable the quantification of disease extent and the prediction of disease course with higher precision than is possible with the human eye.  ...  The supplement containing this article is not sponsored by industry. Open Access.  ... 
doi:10.1007/s00117-018-0407-3 pmid:29922965 fatcat:squun6ggtffsvlde34uwi257py
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