TY  - JOUR
TT  - Data Mining Techniques in Database Systems
AU  - Lico, Ledion
PY  - 2017
DA  - February
JF  - European Journal of Sustainable Development Research
JO  - EJSDR
PB  - CNR GROUP PUBLISHING
WT  - DergiPark
SN  - 2458-8091
SP  - 43
EP  - 50
VL  - 2
IS  - 1
KW  - Data Mining
KW  - BI
KW  - OLAP
KW  - AI
N2  - At the current stage thetechnologies for generating and collecting data have been advancing rapidly.The main problem is the extraction of valuable and accurate information fromlarge data sets. One of the main techniques for solving this problem is Data Mining.Data mining (DM) is the process of identification and extraction of usefulinformation in typically large databases. DM aims to automatically discover theknowledge that is not easily perceivable. It uses statistical analysis and  artificial intelligence (AI) techniques  together to address the issues. There aredifferent types of tasks associated to data mining process. Each task can bethought of as a particular kind of problem to be solved by a data mining algorithm.The main types of tasks performed by DM algorithms are:•   Classification:•   Association:•   Clustering:•   Regression:•   AnomalyDetection:•   FeatureExtraction• Time Series AnalysesIn this paper we will perform a survey ofthe techniques above. A secondary goal of our paper is to give an overview ofhow DM is integrated in Business Intelligence (BI) systems  .BI refers to a set of tools used formultidimensional data analysis, with the main purpose to facilitate decisionmaking. One of the main components of BI systems is OLAP. The main OLAPcomponent is the data cube which is a multidimensional database model that withvarious techniques has accomplished an incredible speed-up of analyzing andprocessing large data sets. We will discuss the advantages of integrating DMtools in BI systems.
CR  - Frans Coenen  ,Data Mining: Past, Present and Future, The Knowledge Engineering Review, 2004, Cambridge University Press
CR  - Pradnya P. Sondwale, Overview of Predictive and Descriptive Data Mining Techniques,  International Journal of Advanced Research in  Computer Science and Software Engineering,April 2015
CR  - Mihika Shah, Sindhu Nair , A Survey of Data Mining Clustering Algorithms, International Journal of Computer Applications (0975 – 8887) Volume 128 – No.1, October 2015
CR  - Irina Tudor, Association Rule Mining as a Data Mining Technique, Petroleum-Gas University of Ploieşti,Buletin Vol. LX No. 1/2008
CR  - Trupti A. Kumbhare et al An Overview of Association Rule Mining Algorithms, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014, 927-930
CR  - N. Elavarasan, Dr. K.Mani,A Survey on Feature Extraction Techniques, International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 1, January 2015
CR  - Fabricio Voznika Leonardo Viana“DATA MINING CLASSIFICATION” Springer, 2001
CR  - A.Shameem Fathima,D.Manimegalai,Nisar Hundewale ,A Review of Data Mining Classification Techniques Applied for Diagnosis and Prognosis of the Arbovirus-Dengue  IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 3, November 2011
CR  - Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly Detection: A Survey. Technical report, University of Minnesota, 2007.
CR  - Victoria Hodge and Jim Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, 22:85126, 2004
CR  - Han - Data Mining Concepts and Techniques 3rd Edition - 2012
CR  - Shanta Rangaswamy, Time Series Data Mining Tool, International Journal of Research in Computer and Communication Technology, Vol 2, Issue 10, October- 2013
CR  - Zanaj,Lico A multidimensional analyses in Business Intelligence systems IJCSIS May 2012
CR  - [14]J. Han, “Towards online analytical mining in large databases,” ACM SIGMOD Record, vol. 27, no. 1, pp. 97-107, March 1998.
CR  - [15] J. Han, S. H. S. Chee and J. Y. Chiang, “Issues for online analytical mining of data warehouses,” in Proc. Of the SIGMOND Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD), Seattle, 1998, pp. 2:1-2:5.
CR  - [16] H. Zhu, “Online analytical mining of association rules,” Master Thesis, Simon Fraser University, 1998,
CR  - [17].S. Dzeroski, D. Hristovski and B. Peterlin, “Using data mining and OLAP to discover patterns in a database of patients with Y chromosome deletions,”in Proc. AMIA Symp., 2000, pp. 215–219.
CR  - [18] F. Dehne, T. Eavis and A. Rau-Chaplin, “Coarse grained parallel on-line analytical processing (OLAP) for data mining, in Proc. of the Int’l Conf. on Computational Science (ICCS), 2001, 589-598.
CR  - [19] Usman ,Asghar,An Architecture for Integrated Online Analytical Mining, JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 3, NO. 2, MAY 2011
CR  - [20] Han - Data Mining Concepts and Techniques 2rd Edition - 2006.pdf
CR  - [21] NGUYEN Feature Extraction for Outlier Detection in High-Dimensional Spaces, JMLR: Workshop and Conference Proceedings 10: 66-75 The Fourth Workshop on Feature Selection in Data Mining
UR  - https://dergipark.org.tr/en/pub/ejsdr/issue//294862
L1  - https://dergipark.org.tr/en/download/article-file/279543
ER  -