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\nThe Industria
l Conference on Data Mining ICDM is held on yearly basis.
\n
\nR
esearcher and Industrial People from different fields will present theoret
ical aspects and their applications\, and the results obtained by applying
data mining. Besides that\, newcomers in the field can get a fast introdu
ction to Data Mining by taking the tutorial running in connection with the
conference.
\n
\nIn a special industry session practicioners fr
om different industrial branches can present their ongoing projects and di
scuss their work with the auditorium. An industrial exhibition\, where com
panies present their data-mining tools\, will round up the conference.
\n
\nFour workshops are running in connection with the conference:
Data Mining in Life Sciences DMLS\, Case-Based Reasoning CBR-MD\, Data Min
ing in Marketing\, and Workshop Data Mining in Agriculture. Other proposal
s for workshops are welcome and can be submitted to info@data-mining-forum
.de.
\n
\nIn a problem/solution hour you will have the opportuni
ty to present your application and ask for support by researchers.
\n
\nThe social events will give you the opportunity to meet top leadin
g researchers in Data Mining and Machine Learning from all over the world.
\n
\nTarget groups of ICDM
\nResearchers
\nResearcher
s doing theoretical and applied research in data mining.
\n
\nPr
acticioners
\nPracticioners from different industrial\, social or eco
nomic branches such as for example:
\n
\nMarketing
\nDataba
se marketing companys\, direct marketing companys\, credit scoring service
providers\, Marketing service providers\, mailorderhouses\, insurance com
panys\, financial service providers marketing agenturen\, e-commerce compa
nys
\n
\nManagers and specialists for analytical CRM\, database
marketing\, sales\, operations research\, produktmanagement and IT
\n
\nCustomer-Relationship Managment
\nManagers and specialists fo
r analytical CRM\, database marketing\, sales\, operations research\, prod
uktmanagement and IT
\n
\nIndustry Automation Engineers dealing
with the automation of industrial processes\, where a lot of data have to
be summarized to give new guidelines for process control\, product develop
ment\, and quality assurance
\n
\nMedicine
\nPhysicians and
veterinarians\, who have to convert subjective data (i.e. the colouring i
n histology and cytology) into objective clinical results.
\n
\n
System Biology\, Genetics
\nSystem Biology\, Geneticists\, who\, in t
he scope of Genomics and Proteomics\, have to detect the minority of disea
se-relevant data out of millions of poorly understood basis data.
\n<
br />\nPublic Sector\, Finance
\nFinancal Manager\, economists and la
wyers engaged in politics and management\, who have to develop new guideli
nes and payment directives in the framework of the health reform law and a
re to develop means of decision making\, which are logically deducible fro
m data bases\, documents and pragmatic approaches.
\nThe Industrial c
onferences on Data Mining ICDM is held on yearly basis. Experts from diffe
rent fields will present their applications and the results obtained by ap
plying data mining. Besides that\, newcomers in the field can get a fast i
ntroduction to Data Mining by taking the tutorial running in connection wi
th the conference. In a problem/solution hour you will have the opportunit
y to present your application and ask for support by others or for coopera
tion in solving the problem.
\n
\n«\; top
\n
\nTo
pics of the conference
\nPaper submissions should be related but not
limited to any of the following topics:
\n
\nApplications of Dat
a Mining in ...
\n
\nMarketing
\nMedicine
\nCivil Engi
neering
\nE-Commerce (Mining Logfiles)
\nBiotechnology
\nQu
ality Management
\nMultimedia Data (Image\, Video\, Text\, Signals)\nWeb-Mining
\nIntrusion Detection in Networks
\nCriminology
\nTelecommunications
\nSocial Sciences
\nForensic Data Ana
lysis
\nDrug Discovery
\nAgriculture
\nSmart Maintenance
\nLegal Court Cases
\nEnergy Industries
\nLogistics and Suppl
y Chain Management
\nFinance and Stock Markets
\nMeterology and
more ...
\n
\nTheoretical and Application-oriented Topics in ...
\n
\nBig Data and Algorithm for Big Data
\nCase-Based Reas
oning and Similarity-Based Reasoning
\nClustering
\nClassificati
on and Prediction
\nStatistical Learning
\nAssociation Rules
\nDeviation and Novelty Detection
\nControl Charts
\nConceptio
nal Learning
\nGoodness Measures and Evaluation (e.g. false discovery
rates)
\nInductive Learning Including Decision Tree and Rule Inducti
on Learning
\nOrganisational Learning and Evolutional Learning
\
nSampling Methods
\nSimilarity Measures and Learning of Similarity
\nStatistical Learning and Neural Net Based Learning
\nVisualizati
on and Data Mining
\nDeviation and Novelty Detection
\nFeature G
rouping\, Discretization\, Selection and Transformation
\nFeature Lea
rning
\nFrequent Pattern Mining
\nLearning and Adaptive Control<
br />\nLearning/Adaption of Recognition and Perception
\nLearning for
Handwriting Recognition
\nLearning in Image Pre-Processing and Segme
ntation
\nMining Financial or Stockmarket Data
\nMining Motion f
rom Sequence
\nSubspace Methods
\nSupport Vector Machines
\
nTime Series and Sequential Pattern Mining
\nDesirabilities
\nGr
aph Miningbigdata
\nAgent Data Mining
\nApplicatio
\n
\nURL:
\n
\nTickets: https://go.evvnt.com/813231-1?pid=4528
SUMMARY:22nd Industrial Conference on Data Mining ICDM 2022
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SUMMARY:22nd Industrial Conference on Data Mining ICDM 2022
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