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MLDM 2018 - 14th International Conference on Machine Learning and Data Mining MLDM 2018

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Category Machine Learning; Data Mining; Classification; Image Mining; Big Data; Clustering; Frequent Item Set Mining; Time-Series Mining; Pattern Recognition

Deadline: January 15, 2018 | Date: July 14, 2018-July 17, 2018

Venue/Country: New York, U.S.A

Updated: 2017-08-13 19:10:30 (GMT+9)

Call For Papers - CFP

The Aim of the Conference

The aim of the conference is to bring together researchers from all over the world who deal with machine learning and data mining in order to discuss the recent status of the research and to direct further developments. Basic research papers as well as application papers are welcome.

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Topics of the conference

All kinds of applications are welcome but special preference will be given to multimedia related applications, applications from live sciences and webmining.

Paper submissions should be related but not limited to any of the following topics:

association rules

case-based reasoning and learning

classification and interpretation of images, text, video

conceptional learning and clustering

Goodness measures and evaluaion (e.g. false discovery rates)

inductive learning including decision tree and rule induction learning

knowledge extraction from text, video, signals and images

mining gene data bases and biological data bases

mining images, temporal-spatial data, images from remote sensing

mining structural representations such as log files, text documents and HTML documents

mining text documents

organisational learning and evolutional learning

probabilistic information retrieval

Sampling methods

Selection with small samples

similarity measures and learning of similarity

statistical learning and neural net based learning

video mining

visualization and data mining

Applications of Clustering

Aspects of Data Mining

Applications in Medicine

Autoamtic Semantic Annotation of Media Content

Bayesian Models and Methods

Case-Based Reasoning and Associative Memory

Classification and Model Estimation

Content-Based Image Retrieval

Decision Trees

Deviation and Novelty Detection

Feature Grouping, Discretization, Selection and Transformation

Feature Learning

Frequent Pattern Mining

High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry

Learning and adaptive control

Learning/adaption of recognition and perception

Learning for Handwriting Recognition

Learning in Image Pre-Processing and Segmentation

Learning in process automation

Learning of internal representations and models

Learning of appropriate behaviour

Learning of action patterns

Learning of Ontologies

Learning of Semantic Inferencing Rules

Learning of Visual Ontologies

Learning robots

Mining Images in Computer Vision

Mining Images and Texture

Mining Motion from Sequence

Neural Methods

Network Analysis and Intrusion Detection

Nonlinear Function Learning and Neural Net Based Learning

Real-Time Event Learning and Detection

Retrieval Methods

Rule Induction and Grammars

Speech Analysis

Statistical and Conceptual Clustering Methods

Statistical and Evolutionary Learning

Subspace Methods

Support Vector Machines

Symbolic Learning and Neural Networks in Document Processing

Time Series and Sequential Pattern Mining

Audio Mining

Cognition and Computer Vision


Classification & Prediction

Statistical Learning

Association Rules


Design of Experiment

Strategy of Experimentation

Capability Indices

Deviation and Novelty Detection

Control Charts

Design of Experiments

Capability Indices

Conceptional Learning

Goodness Measures and Evaluation (e.g. false discovery rates)

Inductive Learning Including Decision Tree and Rule Induction Learning

Organisational Learning and Evolutional Learning

Sampling Methods

Similarity Measures and Learning of Similarity

Statistical Learning and Neural Net Based Learning

Visualization and Data Mining

Deviation and Novelty Detection

Feature Grouping, Discretization, Selection and Transformation

Feature Learning

Frequent Pattern Mining

Learning and Adaptive Control

Learning/Adaption of Recognition and Perception

Learning for Handwriting Recognition

Learning in Image Pre-Processing and Segmentation

Mining Financial or Stockmarket Data

Mining Motion from Sequence

Subspace Methods

Support Vector Machines

Time Series and Sequential Pattern Mining


Graph Mining

Agent Data Mining

Applications in Software Testing

Authors can submit their paper in long or short version.

Long Paper

The paper must be formatted in the Springer LNCS format. They should have at most 15 pages. The papers will be reviewed by the program committee. Accepted long papers will be published by Springer Verlag in the LNAI Series in the book Advances in Data Mining, edited by Petra Perner.

Short Paper

Short papers are also welcome and can be used to describe work in progress or project ideas. They can have 5 to max. 15 pages, formatted in Springer LNCS format. Accepted short papers will be presented as poster in the poster session. They will be published in a special poster proceedings book.

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Program Committee


Petra Perner IBaI Leipzig, Germany


Sergey Ablameyko Belarus State University, Belarus

Reneta Barneva The State University of New York at Fredonia, USA

Michelangelo Ceci Universtiy of Bari, Italy

Patrick Bouthemy INRIA VISTA, France

Xiaoqing Ding Tsinghua University, China

Christoph F. Eick Universtiy of Houston, USA

Ana Fred Technical University of Lisboa, Portugal

Giorgio Giacinto University of Cagliari, Italy

Makato Haraguchi Hokkaido University of Sapporo, Japan

Dimitris Karras Chalkis Institute of Technology, Greece

Adam Krzyzak Concordia University, Canada

Thang V. Pham University of Amsterdam, The Netherlands

Linda Shapiro University of Washington, USA

Tamas Sziranyi MTA-SZTAKI, Hungary

Francis E.H. Tay National University of Singapore, Singapore

Alexander Ulanov HP Labs, Russia

Zeev Volkovich ORT Braude College of Engineering, Israel

Patrick Wang Northeastern University, USA

An industrial exhibition running in connection with the conference will give you the opportunity to look at new trends and systems in industry and to present your research to industry.

Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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