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Data Mining and Big Data

First International Conference, DMBD 2016, Bali, Indonesia, June 25-30, 2016. Proceedings

  • Conference proceedings
  • © 2016

Overview

Editors:
  1. Ying Tan

    1. Peking University, Beijing, China

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  2. Yuhui Shi

    1. Xi'an Jiaotong-Liverpool University, Suzhou, China

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9714)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: DMBD 2016.

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Table of contents (57 papers)

  1. Front Matter

    Pages I-XVI
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  2. Challenges in Data Mining and Big Data

    1. Front Matter

      Pages 1-1
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    2. Evolutionary Computation and Big Data: Key Challenges and Future Directions

      • Shi Cheng, Bin Liu, Yuhui Shi, Yaochu Jin, Bin Li
      Pages 3-14

    3. Prospects and Challenges in Online Data Mining

      • Maxim Bakaev, Tatiana Avdeenko
      Pages 15-23

  3. Data Mining Algorithms

    1. Front Matter

      Pages 25-25
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    2. Enhance AdaBoost Algorithm by Integrating LDA Topic Model

      • Fangyu Gai, Zhiqiang Li, Xinwen Jiang, Hongchen Guo
      Pages 27-37

    3. An Improved Algorithm for MicroRNA Profiling from Next Generation Sequencing Data

      • Salim A., Amjesh R., Vinod Chandra S.S.
      Pages 38-47

    4. Utilising the Cross Industry Standard Process for Data Mining to Reduce Uncertainty in the Measurement and Verification of Energy Savings

      • Colm V. Gallagher, Ken Bruton, Dominic T. J. O’Sullivan
      Pages 48-58

    5. Implementing Majority Voting Rule to Classify Corporate Value Based on Environmental Efforts

      • Ratna Hidayati, Katsutoshi Kanamori, Ling Feng, Hayato Ohwada
      Pages 59-66

    6. Model Proposal of Knowledge Management for Technology Based Companies

      • Jorge Leonardo Puentes Morantes, Nancy Yurani Ortiz Guevara, José Ignacio Rodriguez Molano
      Pages 67-74

  4. Frequent Itemset Mining

    1. Front Matter

      Pages 75-75
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    2. Oracle and Vertica for Frequent Itemset Mining

      • Hristo Kyurkchiev, Kalinka Kaloyanova
      Pages 77-85

    3. Reconstructing Positive Surveys from Negative Surveys with Background Knowledge

      • Dongdong Zhao, Wenjian Luo, Lihua Yue
      Pages 86-99

  5. Spatial Data Mining

    1. Front Matter

      Pages 101-101
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    2. Application of the Spatial Data Mining Methodology and Gamification for the Optimisation of Solving the Transport Issues of the “Varsovian Mordor”

      • Robert Olszewski, Agnieszka Turek
      Pages 103-114

    3. A Geo-Social Data Model for Moving Objects

      • Hengcai Zhang, Feng Lu, Jie Chen
      Pages 115-122

    4. Optimization on Arrangement of Precaution Areas Serving for Ships’ Routeing in the Taiwan Strait Based on Massive AIS Data

      • Jinhai Chen, Feng Lu, Mingxiao Li, Pengfei Huang, Xiliang Liu, Qiang Mei
      Pages 123-133

  6. Prediction

    1. Front Matter

      Pages 135-135
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    2. Bulk Price Forecasting Using Spark over NSE Data Set

      • Vijay Krishna Menon, Nithin Chekravarthi Vasireddy, Sai Aswin Jami, Viswa Teja Naveen Pedamallu, Varsha Sureshkumar, K. P. Soman
      Pages 137-146

    3. Prediction and Survival Analysis of Patients After Liver Transplantation Using RBF Networks

      • C. G. Raji, S. S. Vinod Chandra
      Pages 147-155
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Other volumes

  1. Data Mining and Big Data

Keywords

About this book

The LNCS volume LNCS 9714 constitutes the refereed proceedings of the International Conference on Data Mining and Big Data, DMBD 2016, held in Bali, Indonesia, in June 2016.
The 57 papers presented in this volume were carefully reviewed and selected from 115 submissions. The theme of DMBD 2016 is "Serving Life with Data Science". Data mining refers to the activity of going through big data sets to look for relevant or pertinent information.The papers are organized in 10 cohesive sections covering all major topics of the research and development of data mining and big data and one Workshop on Computational Aspects of Pattern Recognition and Computer Vision.

Editors and Affiliations

  • Peking University, Beijing, China

    Ying Tan

  • Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

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