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Data Mining Application for Finding Patterns: Survey of Large Data Research Tools

Aive Islam
2017 American Journal of Neural Networks and Applications  
Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques.  ...  This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.  ...  Current literature considers heuristic methods for analyzing click stream data generated by websites.  ... 
doi:10.11648/j.ajnna.20170302.11 fatcat:mjqek24re5faxbxfax4xgtwebq

Discovering common motifs in cursor movement data for improving web search

Dmitry Lagun, Mikhail Ageev, Qi Guo, Eugene Agichtein
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
To address this problem, we introduce a novel approach of automatically discovering frequent subsequences, or motifs, in mouse cursor movement data.  ...  In order to scale our approach to realistic datasets, we introduce novel optimizations for motif discovery, specifically designed for mining cursor movement data.  ...  Figure 3 : 3 Recall of finding similar motifs vs. Pruning Power for LB_Keogh and DM methods, shown for representative values of .  ... 
doi:10.1145/2556195.2556265 dblp:conf/wsdm/LagunAGA14 fatcat:3mhdutdeyna27iaywukze7li7q

DeepScreening: a deep learning-based screening web server for accelerating drug discovery

2019 Database: The Journal of Biological Databases and Curation  
perform virtual screening either the chemical probes or drugs for a specific target of interest.  ...  The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or diverse chemical libraries in stock.  ...  Conflict of Interest: none declared.  ... 
doi:10.1093/database/baz104 pmid:31608949 pmcid:PMC6790966 fatcat:37lcdva7dngbfexyvqcnfllvre

Analysis of Gene Expression Data Using BRB-Array Tools

Richard Simon, Amy Lam, Ming-Chung Li, Michael Ngan, Supriya Menenzes, Yingdong Zhao
2007 Cancer Informatics  
The software is designed for use by biomedical scientists who wish to have access to state-of-the-art statistical methods for the analysis of gene expression data and to receive training in the statistical  ...  The software provides the most extensive set of tools available for predictive classifi er development and complete cross-validation.  ...  Yi Xia who have contributed to the development of BRB-ArrayTools.  ... 
doi:10.1177/117693510700300022 fatcat:tcijueumujccnlogubjxd5pauu

Robust high-throughput phenotyping with deep segmentation enabled by a web-based annotator [article]

Jialin Yuan, Damanpreet Kaur, Zheng Zhou, Michael Nagle, Nihar A. Doshi, Ali Behnoudfar, Ekaterina Peremyslova, Cathleen Ma, Steven H. Strauss, Fuxin Li
2022 bioRxiv   pre-print
As a case study in the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of planta regeneration in Populus  ...  We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration  ...  We thank the members of the GREAT TREES (Genetic Research on Engineering and Advanced Transformation of Trees) Research Cooperative at Oregon State University for its long term investment in our transformation  ... 
doi:10.1101/2022.03.11.483823 fatcat:k4nxbwlg2fbbxdhubodxtstofi

A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

Giuseppe Agapito, Pietro Guzzi, Mario Cannataro
2018 High-Throughput  
A use case in pharmacogenomics is presented.  ...  Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel  ...  NGS Next Generation Sequencing  ... 
doi:10.3390/ht7020017 pmid:29904017 pmcid:PMC6023446 fatcat:bkzfvszgifh5xi7k3qdyfhbf4u

User Response Prediction in Online Advertising [article]

Zhabiz Gharibshah, Xingquan Zhu
2021 arXiv   pre-print
Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction.  ...  The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions  ...  of click-through rate on ads or user interactions for purchasing a product, i.e. a conversion.  ... 
arXiv:2101.02342v2 fatcat:clgefamcd5fmbeg5ephizy3zqu

Preface to the special issue on data mining for personalization

Bamshad Mobasher, Alexander Tuzhilin
2008 User modeling and user-adapted interaction  
As a result of user interactions with these resources, tremendous volumes of clickstream, transaction, and user data are collected by organizations in their daily operations.  ...  Automatic personalization is a central technology used in user-adaptive systems to deliver dynamic content, such as textual information, links, advertisements, and product recommendations that are tailored  ...  The authors leverage a topical ontology for estimating users' topic preferences based on past queries and click-throughs on query results.  ... 
doi:10.1007/s11257-008-9060-2 fatcat:wktxkjytsbgnbhdevplo6s6wwu

Mining business databases

Ronald J. Brachman, Tom Khabaza, Willi Kloesgen, Gregory Piatetsky-Shapiro, Evangelos Simoudis
1996 Communications of the ACM  
Acknowledgments We thank Usama Fayyad and Sam Uthurusamy for encouraging us to write this article, Padhraic Smyth for his ideas on data mining tasks, and Robert Golan for help on financial applications  ...  MDT also allows automatic report generation (in HTML form), helping users understand the causes of changes and to point and click to drill down into more detailed analyses.  ...  THE AMOUNT OF DATA COLLECTED AND WAREHOUSED IN ALL INDUSTRIES IS GROWING AT a phenomenal rate.  ... 
doi:10.1145/240455.240468 fatcat:23dbhk7wtvd3jajrw7xxtdges4

Application of the SwissDrugDesign Online Resources in Virtual Screening

Antoine Daina, Vincent Zoete
2019 International Journal of Molecular Sciences  
The present review aims at providing a short description of these methods together with examples of their application in virtual screening, where SwissDrugDesign tools successfully supported the discovery  ...  This project provides a collection of freely available online tools for computer-aided drug design.  ...  We thank the SIB Swiss Institute of Bioinformatics, the Swiss National Science Foundation and SystemsX for financial support. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijms20184612 pmid:31540350 pmcid:PMC6770839 fatcat:rlfj6rp4bncerpsicnzcjjopym

Perspectives on Supercomputing and Artificial Intelligence Applications in Drug Discovery

2020 Supercomputing Frontiers and Innovations  
The evolution results in big data accumulated in life sciences and the fields of drug discovery.  ...  The big data demands for supercomputing in biology and medicine, although the computing complexity is still a grand challenge for sophisticated biosystems in drug design in this supercomputing era.  ...  Guangzhou (201604020109), the Guangdong Provincial Key Lab. of New Drug Design and Evaluation (Grant 2011A060901014) for funding.  ... 
doi:10.14529/jsfi200302 fatcat:577xucm4sbdmvbg5oymt4vp2ym

Personalised online sales using web usage data mining

Xuejun Zhang, John Edwards, Jenny Harding
2007 Computers in industry (Print)  
Through the stages of pattern discovery in the K-means data mining process, nineteen generic clusters are generated. One cluster containing only one candidate has been filtered out.  ...  The system is capable of dealing with transaction logs in a non-intrusive manner, collecting interaction data about the Web searching process from multiple users, through employing a collaborative approach  ... 
doi:10.1016/j.compind.2007.02.004 fatcat:z6c3763w75c5nitbotr4cplyfi

A Work-Centered Visual Analytics Model to Support Engineering Design with Interactive Visualization and Data-Mining

Xin Yan, Mu Qiao, Jia Li, Timothy W. Simpson, Gary M. Stump, Xiaolong (Luke) Zhang
2012 2012 45th Hawaii International Conference on System Sciences  
The proposed system allows designers to interactively examine large design data sets through visualization and interactively construct data models from automatic data mining algorithms.  ...  that are critical for in-depth analysis.  ...  Acknowledgement The authors acknowledge support by a grant from National Science Foundation (CCF XXXXXXX).  ... 
doi:10.1109/hicss.2012.87 dblp:conf/hicss/YanQLSSZ12 fatcat:sli6r37mobgbviafq5vxl4jcaa

Contextual Search: A Computational Framework

Massimo Melucci
2012 Foundations and Trends in Information Retrieval  
The growing availability of data in electronic form, the expansion of the World Wide Web (WWW) and the accessibility of computational methods for large-scale data processing have allowed researchers in  ...  In this survey we provide a background to the subject by: placing it among other surveys on relevance, interaction, context, and behavior; providing the description of the contextual variables used for  ...  Acknowledgments I would like to thank the Information Management Systems research group, led by Maristella Agosti, at the Department of Information Engineering for the continuous collaboration; Fabrizio  ... 
doi:10.1561/1500000023 fatcat:bjx5it7en5fapbg6fvbqs6e7jy

Modeling and predicting user behavior in sponsored search

Josh Attenberg, Sandeep Pandey, Torsten Suel
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
Additionally, we develop a generative model to mimic trends in observed user activity using a mixture of pareto distributions.  ...  Implicit user feedback, including click-through and subsequent browsing behavior, is crucial for evaluating and improving the quality of results returned by search engines.  ...  Click-Through Rate A simple estimation of click-through rate for a particular url: The number of times a url is clicked divided by the number of times a url is returned as a query result.  ... 
doi:10.1145/1557019.1557135 dblp:conf/kdd/AttenbergPS09 fatcat:ncbccs3ring5xoascap5sm54oe
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