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Database Issues in Knowledge Discovery and Data Mining

Chris Rainsford, John Roddick
1999 Australasian Journal of Information Systems  
The terms "Knowledge Discovery in Databases" and "Data Mining" have been adopted for a field of research dealing with the automatic discovery of knowledge impb'cit within databases.  ...  , the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge  ...  The term Data Mining, or Knowledge Discovery in Databases (KDD), has been adopted for a field of research dealing with the discovery of information or knowledge from data held in more or less structured  ... 
doi:10.3127/ajis.v6i2.310 fatcat:57zzkqzw2bdgndhjp5tumgicd4

Research Challenges for Data Mining in Science and Engineering [chapter]

Jiawei Han, Jing Gao
2008 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
for new, data-intensive methods to conduct research in science and engineering.  ...  , and understanding of patterns and knowledge, (3) stream data mining, (4) mining moving object data, RFID data, and data from sensor networks, (5) spatiotemporal and multimedia data mining, (6) mining  ...  data mining: Data mining by integration of sophisticated scientific and engineering domain knowledge Besides general data mining methods and tools for science and engineering, each scientific or engineering  ... 
doi:10.1201/9781420085877.pt1 fatcat:ljs2uybdofgkxfpouawfekdaz4

Aspects of knowledge discovery in technical data

Steffen Brueckner, Stephan Rudolph, Belur V. Dasarathy
2002 Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV  
Keywords: knowledge discovery in scientific data, data mining, dimensional analysis THE KNOWLEDGE DISCOVERY PROCESS FOR SCIENTIFIC DATA Based on the standard knowledge discovery (data mining) process 4  ...  This paper emphasizes the need for a modified knowledge discovery process for engineering (and other scientific domains) and shows the differences to the traditional knowledge discovery in data bases.  ...  In the following chapters these knowledge discovery steps and the implications for the use with scientific data are discussed in more detail.  ... 
doi:10.1117/12.460218 dblp:conf/dmkdttt/BrucknerR02 fatcat:x3zyvhnf7fa6bm3gce2dl2du6e

Concept Formation in Scientific Knowledge Discovery from a Constructivist View [chapter]

Wei Peng, John S. Gero
2009 Scientific Data Mining and Knowledge Discovery  
This chapter argues that the computer-aided scientific knowledge discovery tool should facilitate scientific knowledge development through assisting scientists to build first-person knowledge and thirdperson  ...  A number of challenges for designing such a system have been discussed.  ...  in model construction in the scientific knowledge discovery and Knowledge Discovery in Databases (KDD) area [56] , [57] .  ... 
doi:10.1007/978-3-642-02788-8_5 fatcat:fmm2sepq4vg2hkagvbmusrtiam

A Novel Model for Global Customer Retention Using Data Mining Technology [chapter]

Jie Lin, Xu Xu
2009 Data Mining and Knowledge Discovery in Real Life Applications  
This chapter deals with how to use data mining technology to find interesting pattern, which can be organized for global customer retention.  ...  Customer data and information technology (IT) tools shape into the foundation upon which any successful CRM strategy is built.  ...  Data Mining and Knowledge Discovery in Real Life Applications 252 usage behavior.  ... 
doi:10.5772/6453 fatcat:5g5ix2bk7bh7je7zhxaegw7tgi

FROM DATA MINING AND KNOWLEDGE DISCOVERY TO BIG DATA ANALYTICS AND KNOWLEDGE EXTRACTION FOR APPLICATIONS IN SCIENCE

Subana Shanmuganathan
2014 Journal of Computer Science  
Data mining" for "knowledge discovery in databases" and associated computational operations first introduced in the mid-1990 s can no longer cope with the analytical issues relating to the so-called "big  ...  In view of the above facts, the paper gives an introduction to the synergistic challenges in "data-intensive" science and "exascale" computing for resolving "big data analytics" and "data science" issues  ...  INTRODUCTION "Data mining" for "knowledge discovery in databases" and associated computational operations first introduced in the mid-1990 s can no longer be used to analyse the so-called "big data".  ... 
doi:10.3844/jcssp.2014.2658.2665 fatcat:whtc2on6vncq5hayilctdfpvgu

Redescription Mining and Applications in Bioinformatics [chapter]

Naren Ramakrishnan, Mohammed Zaki
2009 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
We present algorithms for redescription mining based on formal concept analysis and applications of redescription mining to multiple biological datasets.  ...  ., genes, proteins) and a collection of descriptors defined over this set, the goal of redescription mining is to use the given descriptors as a vocabulary and find subsets of data that afford multiple  ...  Acknowledgements This work was supported in part by NSF grants CNS-0615181, ITR-0428344, EMT-0829835, and CNS-0103708, and NIH Grant 1R01EB0080161-01A1.  ... 
doi:10.1201/9781420086850.ch22 fatcat:lelc5qyxrzggnkuahgu7ticihe

Big data, big results: Knowledge discovery in output from large-scale analytics

Tyler H. McCormick, Rebecca Ferrell, Alan F. Karr, Patrick B. Ryan
2014 Statistical analysis and data mining  
We focus on the value of knowledge discovery methods and the challenges in extracting clinically relevant knowledge from big results.  ...  We believe our analyses are both scientifically and methodologically valuable as they reveal information about how methods/algorithms perform under various circumstances, as well as provide a basis for  ...  Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1002/sam.11237 fatcat:rflvfocj7ne5pfbb7conzv6gqq

Rough Set Theory — Fundamental Concepts, Principals, Data Extraction, and Applications [chapter]

Silvia Rissino, Germano Lambert-Torres
2009 Data Mining and Knowledge Discovery in Real Life Applications  
Rough Set is the knowledge discovery or Data Mining in database.  ...  Knowledge Discovery in Database -KDD Knowledge Discovery in Database -KDD is a process, with several stages, no trivial, interactive and iterative, for the identification of comprehensible patterns, valid  ...  Table 9 Conclusion of analysis: In this analysis, no data was excluded. c. Given analysis of condition attributes in Table 9 , it can be observed that the same data exists in proceeding tables.  ... 
doi:10.5772/6440 fatcat:wn2dgx5u3rdk5egnzzmq7ko4my

Simultaneous optimization of complex mining tasks with a knowledgeable cache

Ruoming Jin, Kaushik Sinha, Gagan Agrawal
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
With an increasing use of data mining tools and techniques, we envision that a Knowledge Discovery and Data Mining System (KDDMS) will have to support and optimize for the following scenarios: 1) Sequence  ...  This paper presents a systematic mechanism to optimize for the above cases, targetting the class of mining queries involving frequent pattern mining on one or multiple datasets.  ...  With an increasing use of data mining tools and techniques, we envision that a Knowledge Discovery and Data Mining System (KDDMS) will have to support and optimize for the following scenarios: ¢ Sequence  ... 
doi:10.1145/1081870.1081943 dblp:conf/kdd/JinSA05 fatcat:ktnurrcayjbqfbp7g3g2paxrbq

Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey

Albrecht Zimmermann
2019 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
In more general terms, we call for the development of a true "Data Science" that -based on work in other domains, results in ML, and existing tools -develops needed data generators and builds up the knowledge  ...  values, and 3) work comparing mining results with the ground truth in the data.  ...  Acknowledgments We are grateful to Matthijs van Leeuwen, Arno Siebes, and Jilles Vreeken for reviewing preliminary versions of this article, Marc Plantevit, Wouter Duivesteijn, and Arnaud Soulet for providing  ... 
doi:10.1002/widm.1330 fatcat:f7dvoxeebfbt3dw4ft6kimbi6u

A data mining framework for optimal product selection in retail supermarket data

Tom Brijs, Bart Goethals, Gilbert Swinnen, Koen Vanhoof, Geert Wets
2000 Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00  
In recent years, data mining researchers have developed efficient association rule algorithms for retail market basket analysis.  ...  Therefore, in this paper, the authors present an important generalization of the existing model in order to make it suitable for supermarket data as well, and to enable retailers to add category restrictions  ...  Association rule mining [2] can help retailers to efficiently extract this knowledge from large retail databases. We assume some familiarity with the basic notions of association rule mining.  ... 
doi:10.1145/347090.347156 dblp:conf/kdd/BrijsGSVW00 fatcat:65eu6ltv5zf3bif25qdiumzbpq

Elastic stream processing in the Cloud

Waldemar Hummer, Benjamin Satzger, Schahram Dustdar
2013 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
In contrast to traditional databases, stream processing systems perform continuous queries and handle data on-the-fly.  ...  Stream processing is a computing paradigm that has emerged from the necessity of handling high volumes of data in real time.  ...  These data streams include relevant information that can be revealed by data mining and processing.  ... 
doi:10.1002/widm.1100 fatcat:o67z4wzq75cbboef2ex5a7lmma

Privacy-preserving data integration and sharing

Chris Clifton, Murat Kantarcioǧlu, AnHai Doan, Gunther Schadow, Jaideep Vaidya, Ahmed Elmagarmid, Dan Suciu
2004 Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery - DMKD '04  
Integrating data from multiple sources has been a longstanding challenge in the database community.  ...  Techniques such as privacy-preserving data mining promises privacy, but assume data has integration has been accomplished.  ...  Privacy-preserving integration and sharing of research data in health sciences has become crucial to enabling scientific discovery.  ... 
doi:10.1145/1008694.1008698 dblp:conf/dmkd/CliftonKDSVES04 fatcat:5zotyfq2ercnrkonhc2hh5rgd4

Research on spatial data mining based on uncertainty in Government GIS

Bin Li, Lihong Shi, Jiping Liu
2010 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery  
Uncertainty is the intrinsic property of spatial data and one of important factors affecting the course of spatial data mining.  ...  There are diversiform forms for the essentiality and aspect of uncertainty in the spatial objects of geographic information system.  ...  And all the spatial data used in this paper comes from the database in Government GIS.  ... 
doi:10.1109/fskd.2010.5569275 dblp:conf/fskd/LiSL10 fatcat:szpcsbnfbrfnzniogn4syy4yde
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