Open Science Research Excellence

ICDM 2018 : International Conference on Data Mining

Istanbul, Turkey
July 23 - 24, 2018

Conference Code: 18TR07ICDM

Conference Proceedings

All submitted conference papers will be blind peer reviewed by three competent reviewers. The peer-reviewed conference proceedings are indexed in the Open Science Index, Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, and other index databases. Impact Factor Indicators.

Special Journal Issues

ICDM 2018 has teamed up with the Special Journal Issue on Data Mining. A number of selected high-impact full text papers will also be considered for the special journal issues. All submitted papers will have the opportunity to be considered for this Special Journal Issue. The paper selection will be carried out during the peer review process as well as at the conference presentation stage. Submitted papers must not be under consideration by any other journal or publication. The final decision for paper selection will be made based on peer review reports by the Guest Editors and the Editor-in-Chief jointly. Selected full-text papers will be published online free of charge.

Conference Sponsor and Exhibitor Opportunities

The Conference offers the opportunity to become a conference sponsor or exhibitor. To participate as a sponsor or exhibitor, please download and complete the Conference Sponsorship Request Form.

Important Dates

Abstracts/Full-Text Paper Submission Deadline   May 25, 2018
Notification of Acceptance/Rejection   May 31, 2018
Final Paper (Camera Ready) Submission & Early Bird Registration Deadline   June 22, 2018
Conference Dates   July 23 - 24, 2018

Important Notes

Please ensure your submission meets the conference's strict guidelines for accepting scholarly papers. Downloadable versions of the check list for Full-Text Papers and Abstract Papers.

Please refer to the Paper Submission GUIDE before submitting your paper.

Selected Conference Papers

1) Human Resource Management Practices, Person-Environment Fit and Financial Performance in Brazilian Publicly Traded Companies
Bruno Henrique Rocha Fernandes, Amir Rezaee, Jucelia Appio
2) A Mean–Variance–Skewness Portfolio Optimization Model
Kostas Metaxiotis
3) Using Textual Pre-Processing and Text Mining to Create Semantic Links
Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo
4) The Whale Optimization Algorithm and Its Implementation in MATLAB
S. Adhirai, R. P. Mahapatra, Paramjit Singh
5) Hybrid Reliability-Similarity-Based Approach for Supervised Machine Learning
Walid Cherif
6) Mix Proportioning and Strength Prediction of High Performance Concrete Including Waste Using Artificial Neural Network
D. G. Badagha, C. D. Modhera, S. A. Vasanwala
7) Relay Node Placement for Connectivity Restoration in Wireless Sensor Networks Using Genetic Algorithms
Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri
8) An Improved K-Means Algorithm for Gene Expression Data Clustering
Billel Kenidra, Mohamed Benmohammed
9) A Numerical Description of a Fibre Reinforced Concrete Using a Genetic Algorithm
Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll
10) Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques
Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian
11) Design Approach to Incorporate Unique Performance Characteristics of Special Concrete
Devendra Kumar Pandey, Debabrata Chakraborty
12) CoP-Networks: Virtual Spaces for New Faculty’s Professional Development in the 21st Higher Education
Eman AbuKhousa, Marwan Z. Bataineh
13) Development and Characterization of Re-Entrant Auxetic Fibrous Structures for Application in Ballistic Composites
Rui Magalhães, Sohel Rana, Raul Fangueiro, Clara Gonçalves, Pedro Nunes, Gustavo Dias
14) Application of Data Mining Techniques for Tourism Knowledge Discovery
Teklu Urgessa, Wookjae Maeng, Joong Seek Lee
15) Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method
Dalin Si, Azizan Aziz, Bertrand Lasternas

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