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Distributed Subgroup Mining [chapter]

Michael Wurst, Martin Scholz
2006 Lecture Notes in Computer Science  
We point out substantial differences between these novel learning problems and other kinds of distributed data mining tasks.  ...  In this work we study two natural extensions of classical subgroup discovery to distributed settings.  ...  Discovering Subgroups from Distributed Data Global Distributed Subgroup Mining An extension to classical subgroup discovery that has not yet been investigated by the data mining community is the discovery  ... 
doi:10.1007/11871637_40 fatcat:qdjjcavpizh4tjm4xwxkjmssoe International Journal of Intellectual Advancements and Research in Engineering Computations A NOVEL SECURE MINING OF ASSOCIATION RULES WITH SUBGROUP DISCOVERY IN VERTICALLY DISTRIBUTED DATABASES

Kokilavani, Nandhini, Mohan, T Kalaikumaran
In Our protocols, are based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. In order to improve the performance of the system, proposed the subgroup detection concept in this system.  ...  We propose a protocol for secure mining of association rules in vertically distributed databases. The current protocol is that of Kantarcioglu and Clifton two protocols to be used.  ...  CONCLUSION Fast Distributed Mining (FDM) algorithm is used to fetch frequent item sets in vertically Distributed Databases.  ... 

Subjectively Interesting Subgroup Discovery on Real-Valued Targets

Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamaki, Emilia Oikarinen, Tijl De Bie
2018 2018 IEEE 34th International Conference on Data Engineering (ICDE)  
The succinct subgroup descriptions are in terms of arbitrarily-typed description attributes.  ...  The approach is based on the Subjective Interestingness framework FORSIED to use prior knowledge when mining most informative patterns.  ...  This also allows discovery of subgroups with surprising interactions between targets, a concept known as Exceptional Model Mining.  ... 
doi:10.1109/icde.2018.00148 dblp:conf/icde/LijffijtKDPOB18 fatcat:5ly6ur7aszfodhowr6zhiycwdq

Mining Subgroups with Exceptional Transition Behavior

Florian Lemmerich, Martin Becker, Philipp Singer, Denis Helic, Andreas Hotho, Markus Strohmaier
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
To that end, we introduce first-order Markov chains as a novel model class for exceptional model mining and present a new interestingness measure that quantifies the exceptionality of transition subgroups  ...  Although exceptional model mining provides a well-suited framework for our problem, previously investigated model classes cannot capture transition behavior.  ...  This classical data mining task aims at finding descriptions of data subsets that show an unusual statistical distribution of a target concept.  ... 
doi:10.1145/2939672.2939752 dblp:conf/kdd/Lemmerich0SHHS16 fatcat:lf7v3hg3ojdjjc4n46jwrtwuma

Subjectively Interesting Subgroup Discovery on Real-valued Targets [article]

Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie
2017 arXiv   pre-print
Deriving insights from high-dimensional data is one of the core problems in data mining.  ...  The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.  ...  We further investigated the distribution of the mammals within the subgroups.  ... 
arXiv:1710.04521v1 fatcat:qdqet7ao7fagjlnohvpap3cbbe

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy
2020 Data mining and knowledge discovery  
By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup.  ...  The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups.  ...  with the resulting information: mining deeper leads to subgroups which are no longer actionable.  ... 
doi:10.1007/s10618-020-00674-z fatcat:val2vqsm6bek5fyiwpd365e6my

CSM-SD: Methodology for contrast set mining through subgroup discovery

Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Antonija Krstačić
2009 Journal of Biomedical Informatics  
As a methodological novelty, it is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task.  ...  This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups.  ...  We transform the contrast set mining task to a subgroup discovery task [25, 8, 17, 2] , whose goal is to find descriptions of groups of individuals with unusual distributional characteristics with respect  ... 
doi:10.1016/j.jbi.2008.08.007 pmid:18782633 fatcat:63szynaqh5et3izmgamretrafi

Secure Top-k Subgroup Discovery [chapter]

Henrik Grosskreutz, Benedikt Lemmen, Stefan Rüping
2011 Lecture Notes in Computer Science  
mining.  ...  In this paper, we present a new protocol which allows distributed subgroup discovery while avoiding the disclosure of the individual databases.  ...  As a consequence, the distributed association rule mining protocols cannot be adapted to the task of subgroup discovery, and instead a distributed global subgroup mining protocol has been proposed [24  ... 
doi:10.1007/978-3-642-19896-0_4 fatcat:4s674frggrgktl7qrqfkth2uyy

Exceptional Model Mining

Wouter Duivesteijn, Ad J. Feelders, Arno Knobbe
2015 Data mining and knowledge discovery  
Build up candidate subgroups level-wise, imposing one constraint on one attribute at a time.  ...  to find exceptional subgroups G and associated models M Managing the candidate space in SD and EMM SD and EMM are exploratory techniques.  ...  . , a k for describing subgroups.  ... 
doi:10.1007/s10618-015-0403-4 fatcat:htue3qnofncbbgzm7luxnj62ny

Nugget Browser: Visual Subgroup Mining and Statistical Significance Discovery in Multivariate Datasets

Zhenyu Guo, Matthew O. Ward, Elke A. Rundensteiner
2011 2011 15th International Conference on Information Visualisation  
Subgroup patterns are local findings identifying the subgroups of a population with some unusual, unexpected, or deviating distribution of a target attribute.  ...  In this paper, we present a novel subgroup pattern extraction and visualization system, called the Nugget Browser, that takes advantage of both data mining methods and interactive visual exploration.  ...  It is clear that without the visualization, analysts cannot understand how the subgroups are distributed in the space and the relationships between the subgroups.  ... 
doi:10.1109/iv.2011.21 dblp:conf/iv/GuoWR11 fatcat:dg2pyqulfjgthe6mbhuwd34gjq

Sampling-based sequential subgroup mining

Martin Scholz
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
It allows to turn pattern mining into an iterative process.  ...  Subgroup discovery is a learning task that aims at finding interesting rules from classified examples.  ...  SUBGROUP DISCOVERY Subgroup discovery aims at finding interesting subsets of the instance space that deviate from the overall distribution.  ... 
doi:10.1145/1081870.1081902 dblp:conf/kdd/Scholz05 fatcat:ug2etov4pzbsrmfdqdqlnufkda

Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization

Danlu Liu, William Baskett, David Beversdorf, Chi-Ren Shyu
2019 IEEE journal of biomedical and health informatics  
We have developed a novel subgroup discovery method which employs a deep exploratory mining process to slice and dice thousands of potential subpopulations and prioritize potential cohorts based on their  ...  We also conducted a scaling analysis using a distributed computing environment to suggest computational resource needs for when the subpopulation number increases.  ...  Michael Phinney for his implementation of the distributed version of contrast mining methods, and the University of Missouri Research Computing Support Services (RCSS) group for providing computing support  ... 
doi:10.1109/jbhi.2019.2939149 pmid:31494566 fatcat:n7evwo55u5ebjintvtojfr57yu

Exceptionally Monotone Models -- The Rank Correlation Model Class for Exceptional Model Mining

Lennart Downar, Wouter Duivesteijn
2015 2015 IEEE International Conference on Data Mining  
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way.  ...  We find subgroups with an exceptionally monotone relation between the targets.  ...  Target distribution for subgroups found on the Cern dataset 1 Beam Search for Top-q Exceptional Model Mining(Duivesteijn, 2013; Duivesteijn et al., 2016) Input: Dataset Ω, quality measure ϕ, refinement  ... 
doi:10.1109/icdm.2015.81 dblp:conf/icdm/DownarD15 fatcat:dm4gvr6ojnai3bpact7hf7tl2i

Mining explainable local and global subgraph patterns with surprising densities

Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie
2020 Data mining and knowledge discovery  
The first contribution in this paper is to generalize this type of pattern to densities between a pair of subgroups, as well as between all pairs from a set of subgroups that partition the vertices.  ...  in another subgroup defined by properties Y', ideally relative to their expected connectivity.  ...  w.r.t. the beam width for single-subgroup pattern mining.  ... 
doi:10.1007/s10618-020-00721-9 fatcat:52qeqrhwfvgobknip5eul2qoxq

Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach [chapter]

Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie
2020 Proceedings of the 2020 SIAM International Conference on Data Mining  
This view immediately enables iterative mining of such patterns. Our work generalizes prior work on dense subgraph mining (i.e. subgraphs induced by a single subgroup).  ...  The connectivity of a graph can thus possibly be understood in terms of patterns of the form 'the subgroup of individuals with properties X are often (or rarely) friends with individuals in another subgroup  ...  This is effectively a subgroup discovery approach to dense subgraph mining. Limitations of the state-of-the-art.  ... 
doi:10.1137/1.9781611976236.66 dblp:conf/sdm/DengKLB20 fatcat:iysezdjf6jfhzmcpeizbbr62l4
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