Emerging and Vanishing Association Pattern Mining in Hydroclimatic Datasets

Emerging and vanishing association patterns can be defined as association patterns whose frequencies (supports) get stronger and weaker over time, respectively. Discovering these patterns is important for several application domains such as financial and communication services, public health, and hydroclimatic studies. Classical association pattern mining algorithms do not consider how the strengths of association patterns change over time. An association pattern can be defined as an emerging or vanishing pattern when its support measure changes over time. In this paper, we focus on discovery of time evolving association patterns (i.e., emerging and vanishing association patterns) from datasets. To discover such patterns, a novel algorithm, named as Emerging and Vanishing Association Pattern Miner (EVAPMiner) algorithm, was proposed. The proposed algorithm was evaluated using hydroclimatic dataset of Turkey. The analyses showed that the proposed algorithm successfully detects emerging and vanishing association patterns in hydroclimatic datasets.

[1]  D. Cayan,et al.  The influence of precipitation and temperature on seasonal streamflow in California , 1993 .

[2]  C. Dhanya,et al.  Data mining for evolution of association rules for droughts and floods in India using climate inputs , 2009 .

[3]  D. Nagesh Kumar,et al.  DATA MINING AND ITS APPLICATIONS FOR MODELLING RAINFALL EXTREMES , 2009 .

[4]  E. Kahya,et al.  The analysis of El Niño and La Niña signals in streamflows of Turkey , 2001 .

[5]  Ismail Ari,et al.  Online Association Rule Mining over Fast Data , 2013, 2013 IEEE International Congress on Big Data.

[6]  Sherri K. Harms,et al.  Discovering Associations between Climatic and Oceanic Parameters to Monitor Drought in Nebraska Using Data-Mining Techniques , 2005 .

[7]  Stanley A. Changnon,et al.  Climate-Related Fluctuations in Midwestern Floods during 1921–1985 , 1995 .

[8]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[9]  Filiz Dadaser-Celik,et al.  ASSOCIATIONS BETWEEN STREAM FLOW AND CLIMATIC VARIABLES AT KIZILIRMAK RIVER BASIN IN TURKEY , 2012 .

[10]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[11]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[12]  Min Zhang,et al.  The Strategy of Mining Association Rule Based on Cloud Computing , 2011, 2011 International Conference on Business Computing and Global Informatization.

[13]  David Fuhry,et al.  Migration Motif:A spatial-temporal pattern mining approach for financial markets , 2009 .

[14]  Hong Shua,et al.  MINING ASSOCIATION RULES IN GEOGRAPHICAL SPATIO-TEMPORAL DATA , 2008 .

[15]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[16]  Mete Celik,et al.  Discovery of hydrometeorological patterns , 2014 .

[17]  Sherri K. Harms,et al.  Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA , 2004 .

[18]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[19]  Chad Creighton,et al.  Mining gene expression databases for association rules , 2003, Bioinform..

[20]  Padhraic Smyth,et al.  Business applications of data mining , 2002, CACM.

[21]  K. K. Pathak,et al.  Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin , 2013 .

[22]  David L. Olson,et al.  Introduction to Business Data Mining , 2005 .

[23]  Yi-Ping Phoebe Chen,et al.  Association rule mining to detect factors which contribute to heart disease in males and females , 2013, Expert Syst. Appl..

[24]  Khairullah Khan,et al.  Frequent Patterns Minning of Stock Data Using Hybrid Clustering Association Algorithm , 2009, 2009 International Conference on Information Management and Engineering.

[25]  Jesús S. Aguilar-Ruiz,et al.  Gene association analysis: a survey of frequent pattern mining from gene expression data , 2010, Briefings Bioinform..

[26]  Wee Keong Ng,et al.  Fast online dynamic association rule mining , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[27]  M. Madheswaran,et al.  Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm , 2010, ArXiv.

[28]  Filiz Dadaser-Celik,et al.  Wind speed trends over Turkey from 1975 to 2006 , 2014 .