A Practical Comparison on GIS Data of Two Data Mining Algorithms

This work explores the effectiveness of data mining classification techniques, their advantages and disadvantages. Classification is the largest of the applications, consisting in building models to predict belonging to a set of classes. In this study we compared using Weka tool two of the most known data mining algorithms on a collection of Geographic Information System (GIS), data called Cadastre which consist of a parcel plan from the Dolj area of Romania. From the performed experiments, results that the K-nearest neighbor algorithm works better that the Naïve Bayes algorithm in terms of accuracy.

[1]  Norrozila Sulaiman,et al.  A novel intrusion detection system by using intelligent data mining in weka environment , 2011, WCIT.

[2]  Enrico Feoli,et al.  Using spatial data mining to analyze area-diversity patterns among soil, vegetation, and climate: A case study from Almería, Spain , 2017 .

[3]  K. L. Shunmuganathan,et al.  An improved K-nearest-neighbor algorithm using genetic algorithm for sentiment classification , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].

[4]  Zuhal Tanrikulu,et al.  A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness , 2012, Information Technology and Management.

[5]  Hemlata Channe,et al.  Comparative Study of K-NN , Naive Bayes and Decision Tree Classification Techniques , 2016 .

[6]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[7]  Shichao Zhang,et al.  Efficient kNN classification algorithm for big data , 2016, Neurocomputing.

[8]  Mohammed El Amine Bechar,et al.  Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task , 2016 .

[9]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[10]  Xiaojuan Li,et al.  Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard , 2017, Risk analysis : an official publication of the Society for Risk Analysis.

[11]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[12]  Manpreet Singh,et al.  Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector , 2015 .

[13]  Hui Han,et al.  Fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[14]  Xiaonan Li,et al.  Operations research and data mining , 2008, Eur. J. Oper. Res..