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Domain-Driven Data Mining

Longbing Cao, Chengqi Zhang
2006 International Journal of Data Warehousing and Mining  
Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user  ...  We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns  ...  We appreciate CMCRC and SIRCA for providing data services. Thanks also go to Dr. Lin Li and Jiarui Ni for implementation supports.  ... 
doi:10.4018/jdwm.2006100103 fatcat:wk465g5n5zesrn2srxeoslb7py

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

Mu-Chen Chen
2007 Expert systems with applications  
This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria.  ...  In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules.  ...  Acknowledgements The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. NSC 95-2416-H-009-034-MY3.  ... 
doi:10.1016/j.eswa.2006.08.007 fatcat:ciceajdddnaglp2aoqql63naei

Interestingness measures for data mining

Liqiang Geng, Howard J. Hamilton
2006 ACM Computing Surveys  
Interestingness measures play an important role in data mining, regardless of the kind of patterns being mined.  ...  This survey reviews the interestingness measures for rules and summaries, classifies them from several perspectives, compares their properties, identifies their roles in the data mining process, gives  ...  In this article, we surveyed interestingness measures used in data mining. We summarized nine criteria to determine and define interestingness.  ... 
doi:10.1145/1132960.1132963 fatcat:pkb33tqvlnh53kr2rbdgumr52i

Domain-Driven Data Mining: Challenges and Prospects

Longbing Cao
2010 IEEE Transactions on Knowledge and Data Engineering  
To this end, domain-driven data mining (D 3 M) has been proposed to tackle the above issues, and promote the paradigm shift from "data-centered knowledge discovery" to "domain-driven, actionable knowledge  ...  Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion  ...  ACKNOWLEDGMENTS Thanks are given to Dr. Yanchang Zhao, Dr. Huaifeng Zhang, Mr.  ... 
doi:10.1109/tkde.2010.32 fatcat:bnwjryuoyngfhpwr2ylsuww7lu

Performance data mining

Alois Ferscha, Allen D Malony
2001 Future generations computer systems  
It is clear that user-centric analysis in the traditional execute-measure-modify program development process is limited by the user's ability to both interpret the data and to make informed decisions about  ...  In addition, this formulation aids in defining how performance tools for measurement, analysis, and presentation should be used in support of evolving diagnosis requirements.  ... 
doi:10.1016/s0167-739x(01)00047-4 fatcat:hbdnqkbwt5f67ozkbnwjzmetxq

Expanding the Knowledge Base for more Effective Data Mining

K. Niki Kunene, Heinz Roland Weistroffer
2005 European Conference on Information Systems  
Traditionally, data mining, as part of the knowledge discovery process, relies solely on the information contained in the database to generate patterns.  ...  In this paper, we present a new knowledge discovery method that uses additional decision rules and the analytic hierarchy process (AHP) to conceptualize and structure the domain, thus capturing a broader  ...  Table 1 shows a marked increase in objective interestingness measures between the data mining model using the traditional approach (Model 1) and the data mining model employing our Method which includes  ... 
dblp:conf/ecis/KuneneW05 fatcat:2hlydo42xjfx7cfbl7rovuvlsu

A Parallel Approach to Combined Association Rule Mining

Zaid Makani, Sana Arora, Prashasti Kanikar
2013 International Journal of Computer Applications  
In this paper, a parallel approach to Combined Mining has been implemented that not only generates rules which are "actionable" but also does so in a time period that is lesser than that of the traditional  ...  Data Mining carried out using traditional methodologies of Support-Confidence framework and Association Rule Mining yield an enormous number of inefficient rules or patterns in a certain amount of time  ...  They propose combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods as per requirement  ... 
doi:10.5120/10154-5004 fatcat:iocw56oqonclbaxzyf5yoylzjm

DATA MINING TECHNIQUES:A SURVEY PAPER

Nikita Jain .
2013 International Journal of Research in Engineering and Technology  
The data mining based on Neural Network and Genetic Algorithm is researched in detail and the key technology and ways to achieve the data mining on Neural Network and Genetic Algorithm are also surveyed  ...  In this paper, the concept of data mining was summarized and its significance towards its methodologies was illustrated.  ...  Typically, a descriptive model is found through undirected data mining; i.e. a bottom-up approach where the data "speaks for itself".  ... 
doi:10.15623/ijret.2013.0211019 fatcat:wm6evhppz5ccnlgjqcwiimsmaa

How to Semantically Enhance a Data Mining Process? [chapter]

Laurent Brisson, Martine Collard
2009 Lecture Notes in Business Information Processing  
We detail the role of the ontology and we define a part-way interestingness measure that integrates both objective and subjective criteria in order to eval model relevance according to expert knowledge  ...  This paper presents the KEOPS data mining methodology centered on domain knowledge integration. KEOPS is a CRISP-DM compliant methodology which integrates a knowledge base and an ontology.  ...  In the last step we have introduced IMAK, a part-way interestingness measure that integrates both objective and subjective criteria in order to evaluate models relevance according to expert knowledge.  ... 
doi:10.1007/978-3-642-00670-8_8 fatcat:7vinrnq2l5gk7f2htne27gryia

Data mining techniques for IoT analytics

Я.О. Критська, T.O. Білобородова, І.С. Скарга-Бандурова
2019 ВІСНИК СХІДНОУКРАЇНСЬКОГО НАЦІОНАЛЬНОГО УНІВЕРСИТЕТУ імені Володимира Даля  
In this paper, we present a systematic review of various DM models and discuss the DM techniques applicable to different IoT data.  ...  Data mining (DM) is one of the most valuable technologies enable to identify unknown patterns and make Internet of Things (IoT) smarter.  ...  Basic idea of using data mining for IoT One of the most important questions that knowledge discovery in databases (KDD) and data mining technology can solve is how to transform the data generated or captured  ... 
doi:10.33216/1998-7927-2019-253-5-53-62 fatcat:gtkjbcqr7rgm5hg6dtuxagqf6i

Combined Mining: Discovering Informative Knowledge in Complex Data

Longbing Cao, Huaifeng Zhang, Yanchang Zhao, Dan Luo, Chengqi Zhang
2011 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions  ...  Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either  ...  tables; 3) post analysis and mining; 4) involving multiple methods; and 5) mining multiple data sources.  ... 
doi:10.1109/tsmcb.2010.2086060 pmid:21592913 fatcat:lauqhyopdrao5l4e7g4li5ywsa

THE EVOLUTION OF KDD: TOWARDS DOMAIN-DRIVEN DATA MINING

LONGBING CAO, CHENGQI ZHANG
2007 International journal of pattern recognition and artificial intelligence  
Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information regarding a business issue.  ...  The results show that domain-driven data mining has a potential for further enhancing the actionability of mined patterns in real-world situation.  ...  Data used in this paper is from Australian Centrelink and Capital Market CRC, AC3 and SIRCA.  ... 
doi:10.1142/s0218001407005612 fatcat:6ln5hcaxefaftp44kjdywbpjy4

Mining Rare Associations between Biological Ontologies

Fernando Benites, Svenja Simon, Elena Sapozhnikova, Maureen J. Donlin
2014 PLoS ONE  
In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules.  ...  We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases.  ...  To solve it, data mining techniques such as association analysis may help explore dependencies between multiple ontologies that provide different insights into a certain problem.  ... 
doi:10.1371/journal.pone.0084475 pmid:24404165 pmcid:PMC3880308 fatcat:4xqjeg5w7zes3i4kt77xfutpqa

On Ranking Discovered Rules of Data Mining by Data Envelopment Analysis: Some Models with Wider Applications [chapter]

Mehdi Toloo, Soroosh Nalchigar
2011 New Fundamental Technologies in Data Mining  
In other words, in today's business environment, it is essential to mine vast volumes of data for extracting patterns in order to support superior decision-making.  ...  According to Han & Camber (2001) the major reason that data mining has attracted a great deal of attention in information industry in recent years is due to the wide availability of huge amounts of data  ...  The interestingness of association rules is measured by considering multiple criteria involving support, confidence and domain related measures. This paper uses DEA as a post-processing approach.  ... 
doi:10.5772/13659 fatcat:hf6cjwskyrfudh6tlmxfnyxwyi

Articulating Domain Prior Knowledge Using The Analytic Hierarchy Process for More Relevant Data Mining Patterns

K. Niki Kunene
2006 The International Journal of Computers, Systems and Signals  
To increase the confidence of decision makers in the interestingness of discovered patterns, some researchers believe in the incorporation of domain prior-knowledge into the data mining process.  ...  In this paper, we present a new design artifact, that uses the analytic hierarchy process (AHP) to conceptualize and structure domain prior-knowledge, thus capturing a broader essence of domain knowledge  ...  Acknowledgement: This research was completed while the author studied towards her PhD at the Virginia Commonwealth University in Richmond, Virginia.  ... 
dblp:journals/ijcss/Kunene06 fatcat:o3ircnux2jcwnkkid3vgy6i7nu
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