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ICDE conference 2014 table of contents

2014 2014 IEEE 30th International Conference on Data Engineering  
Milios) 15 Finding Common Ground Among Experts' Opinions on Data Clustering: With Applications in Malware Analysis (Guanhua Yan) 28 Towards Effective and Efficient Mining of Arbitrary Shaped Clusters  ...  Abadi) [Search] ICDE Conference 2014 Table of Contents [Page 2 / 16] Research Session 3: Data Mining I -Outliers and Time Series 76 Scalable Distance-Based Outlier Detection Over High-Volume  ... 
doi:10.1109/icde.2014.6816625 fatcat:pm3vcptvmvb6pcjrbq75awpvtq

Graph-based relational learning

Lawrence B. Holder, Diane J. Cook
2003 SIGKDD Explorations  
While a form of graph-based data mining, GBRL focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data.  ...  Learning from graphs, rather than logic, presents representational issues both in input data preparation and output pattern language.  ...  CONCLUSIONS Graph-based relational learning is a fast-growing field of data mining due to the increasing interest in mining the relational aspects of graph-oriented data.  ... 
doi:10.1145/959242.959254 fatcat:l2j3t2nb4jfgvej5zvzrsbzb3m

Author index

2006 2006 IEEE International Conference on Cluster Computing  
Power-scalable High Performance Cluster Saetrom, Pål The Petacomp Machine -A MIMD Cluster for Parallel Pattern- mining Scott, Stephen L.  ...  MSSG: A Framework for Massive-Scale Semantic Graphs Hartono, Albert Designing High Performance and Scalable MPI Intra-node Communication Support for Clusters Harwood, Aaron SLA-Based Coordinated  ... 
doi:10.1109/clustr.2006.311921 fatcat:vmbbimypuze7ncjqfonu4po5l4

2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32

2021 IEEE Transactions on Knowledge and Data Engineering  
., +, TKDE April 2020 728-738 Scalable Detection of Crowd Motion Patterns. Heldens, S., +, TKDE Jan. 2020 152-164 Sequence Pattern Mining with Variables.  ...  ., +, TKDE Feb. 2020 218-233 Image sequences Scalable Detection of Crowd Motion Patterns.  ... 
doi:10.1109/tkde.2020.3038549 fatcat:75f5fmdrpjcwrasjylewyivtmu

Big graph mining

U. Kang, Christos Faloutsos
2013 SIGKDD Explorations  
How do we find patterns and anomalies in very large graphs with billions of nodes and edges? How to mine such big graphs efficiently?  ...  Mining big graphs leads to many interesting applications including cyber security, fraud detection, Web search, recommendation, and many more.  ...  The views and conclusions are those of the authors and should not be interpreted as representing the official policies, of the U.S.  ... 
doi:10.1145/2481244.2481249 fatcat:fzidqzmctndj3nxh2qw55txyuu

Research Methodology on Web Mining for Malware Detection

Shaik. Irfan Babu, Dr. M.V.P. Chandra Sekhara Rao, G.Nagi Reddy
2014 International Journal of Computer Trends and Technology  
The proposed web mining methodology uses web structure mining, using graph mining for malware detection with a case study proposed on cloud mining.  ...  In this review paper we want to discuss Research Methodology on Web mining for Malware detection.  ...  It starts with important graph algorithms that are central to graph mining and pattern discoveries, and describe how we can implement their fast, scalable versions using a unified framework built on top  ... 
doi:10.14445/22312803/ijctt-v12p131 fatcat:tt4nfblmhfb43a5a5j7hrew2pm

Scalable Data-Driven PageRank: Algorithms, System Issues, and Lessons Learned [chapter]

Joyce Jiyoung Whang, Andrew Lenharth, Inderjit S. Dhillon, Keshav Pingali
2015 Lecture Notes in Computer Science  
Large-scale network and graph analysis has received considerable attention recently. Graph mining techniques often involve an iterative algorithm, which can be implemented in a variety of ways.  ...  The implementation lessons not only guide efficient implementations of many graph mining algorithms, but also provide a framework for designing new scalable algorithms.  ...  This research was supported by NSF grants CCF-1117055 and CCF-1320746 to ID, and by NSF grants CNS-1111766 and XPS-1337281 to KP.  ... 
doi:10.1007/978-3-662-48096-0_34 fatcat:2t3ir5ti2jfmvjadeqzvsem6ne

Indexing and mining topological patterns for drug discovery

Sayan Ranu, Ambuj K. Singh
2012 Proceedings of the 15th International Conference on Extending Database Technology - EDBT '12  
As a result, the problem of indexing and mining structural patterns map to indexing and mining patterns from graph and 3D geometric databases.  ...  After presenting an in-depth survey of the techniques on mining significant subgraph patterns, the tutorial will proceed towards the problem of analyzing 3D geometric structures of molecules.  ...  Typical querying and mining tasks involve clustering of large molecular libraries, developing index structures for fast answering of top-k queries, mining structural patterns, and predicting biological  ... 
doi:10.1145/2247596.2247666 dblp:conf/edbt/RanuS12 fatcat:eik3d5yoxnauzjeukeb6uhdm2u

Towards scalable critical alert mining

Bo Zong, Yinghui Wu, Jie Song, Ambuj K. Singh, Hasan Cam, Jiawei Han, Xifeng Yan
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
While the need for mining critical alerts over large scale alert sequences is evident, most alert analysis techniques stop at modeling and mining the causal relations among the alerts.  ...  Second, we propose two fast heuristic algorithms based on tree sampling techniques.  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or  ... 
doi:10.1145/2623330.2623729 dblp:conf/kdd/ZongWSSCHY14 fatcat:kdfrmmv4a5eivdqdcpwouf4foq

A Comparative Study Of Frequent Subgraph Mining Algorithms

K Lakshmi
2012 International Journal of Advanced Information Technology  
Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences  ...  Frequent subgraph mining is another active research topic in data mining . A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics.  ...  is efficient and scalable in mining frequent jump patterns.  ... 
doi:10.5121/ijitcs.2012.2203 fatcat:qpsc44az4nhafmuekhbngp2eci

Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002

2002 2002 IEEE International Conference on Data Mining 2002 Proceedings ICDM-02  
Sebag Computing Frequent Graph Patterns from Semistructured Data ..................... ....... .............................. 458 N. Vanerik, E. Gudes, and S. E.  ...  Kame1 Mining Top-K Frequent Closed Patterns without Minimum Support 21 1 J. Hon. J. Wang, Y. Lu, and P.  ... 
doi:10.1109/icdm.2002.1183878 fatcat:3iufo7cncbbzbn7cwjme73wrpm

Towards Generic Pattern Mining [chapter]

Mohammed J. Zaki, Nagender Parimi, Nilanjana De, Feng Gao, Benjarath Phoophakdee, Joe Urban, Vineet Chaoji, Mohammad Al Hasan, Saeed Salem
2005 Lecture Notes in Computer Science  
Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets.  ...  DMTL provides a systematic solution to a whole class of common FPM tasks like itemset, sequence, tree and graph mining.  ...  We conduct several experiments to show the scalability and efficiency of DMTL for different pattern types like itemsets, sequences, trees and graphs.  ... 
doi:10.1007/978-3-540-32262-7_1 fatcat:mnkjx3xn65gpdmyhcbi3kligve

Interactive Querying over Large Network Data

Robert Pienta, Acar Tamersoy, Hanghang Tong, Alex Endert, Duen Horng (Polo) Chau
2015 Proceedings of the 20th International Conference on Intelligent User Interfaces Companion - IUI Companion '15  
We focus on three critical aspects: scalable data mining algorithms, graph visualization, and interaction design.  ...  We describe our work towards achieving our overall research goal of designing and developing an interactive querying system for large network data.  ...  However, most works focused on the data mining aspects and algorithmic issues with graph querying, like speed, scalability, and robustness.  ... 
doi:10.1145/2732158.2732192 pmid:25859567 pmcid:PMC4388241 dblp:conf/iui/PientaTTEC15 fatcat:byqzusen7rh25e3yc2a5rwtciu

Direct mining of discriminative and essential frequent patterns via model-based search tree

Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip Yu, Olivier Verscheure
2008 Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08  
However, frequent pattern mining is non-trivial since the number of unique patterns is exponential but many are non-discriminative and correlated.  ...  Currently, frequent pattern mining is performed in two sequential steps: enumerating a set of frequent patterns, followed by feature selection.  ...  More studies are being conducted. (2) M b T is a scalable frequent pattern mining algorithm not limited to just itemsets and sub-graphs.  ... 
doi:10.1145/1401890.1401922 dblp:conf/kdd/FanZCGYHYV08 fatcat:bws5y35mfzfjlopo4k5lbmbspi

Towards Generic Pattern Mining [chapter]

Mohammed J. Zaki, Nilanjana De, Feng Gao, Nagender Parimi, Benjarath Phoophakdee, Joe Urban Vineet Chaoji, Mohammad Al Hasan, Saeed Salem
2005 Lecture Notes in Computer Science  
Fig. 4 . 4 Performance: Itemset, Sequence, Tree and Graph Mining  ...  We define generic data structures to handle various pattern types like itemsets, sequences, trees and graphs, and outline the design and implementation of generic data mining algorithms for FPM, such as  ... 
doi:10.1007/11590316_12 fatcat:nfqpno4q6jgsvin24wsgz2scna
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