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AUSTRALASIAN DATA MINING CONFERENCE

AUSTRALASIAN DATA MINING CONFERENCE

Sydney , Australia

Submission Deadline: Friday 05 Aug 2022

Conference Dates: Dec 12, 2022 - Dec 16, 2022

Research
Impact Score 0.40

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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page.

Research Impact Score: 0.40
Contributing Best Scientists: 6
H5-index:
Papers published by Best Scientists 7
Research Ranking (Computer Science) 1254

Conference Call for Papers

AusDM’22 invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges. Topics of interest include, but are not restricted to:

Applications and Case Studies
Big Data Analytics
Biomedical and Health Data Mining
Data mining techniques in Neuroinformatics
Data Mining in Genomics and Proteomics
Business Analytics
Computational Aspects of Data Mining
Data Integration, Matching and Linkage
Data Mining in Education
Data Mining in Security and Surveillance
Data Preparation, Cleaning and Preprocessing
Data Stream Mining
Deep Learning
Evaluation of Results and their Communication

Overview

Top Research Topics at Australasian Data Mining Conference?

  • Data mining (42.35%)
  • Artificial intelligence (32.90%)
  • Machine learning (18.89%)

The conference was organized to reinforce research efforts on Data mining, Artificial intelligence, Machine learning, Pattern recognition and Cluster analysis. The conference features Data mining research that overlaps with concepts in Scalability. The work on Artificial intelligence addressed in it expands to the thematically related Natural language processing.

Studies on Cluster analysis discussed in Australasian Data Mining Conference link to the field of Information retrieval. Most of the works presented in it deals with Information retrieval but it intersects with the subject of Record linkage.

What are the most cited papers published at the conference?

  • Zero-day malware detection based on supervised learning algorithms of API call signatures (125 citations)
  • Using decision tree for diagnosing heart disease patients (99 citations)
  • SparseDTW: a novel approach to speed up dynamic time warping (91 citations)

Research areas of the most cited articles at Australasian Data Mining Conference:

The published papers mainly tackle studies in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval. The published articles about Decision tree under the umbrella field of Data mining overlap with concepts in Geocoding. The conference publications hold forums on Artificial intelligence that merge themes from other disciplines such as Spelling and Pattern recognition.

What topics the last edition of the conference is best known for?

  • Artificial intelligence
  • Statistics
  • Machine learning

The previous edition focused in particular on these issues:

The topics of Artificial intelligence, Data mining, Machine learning, Pattern recognition and Algorithm are the focal point of discussions in Australasian Data Mining Conference. Topics in Artificial intelligence explored in the conference were investigated in conjunction with research in Social media and Statistical disclosure control. Australasian Data Mining Conference explores issues in Data mining which can be linked to other research areas like Feature (machine learning) and Cluster analysis.

Machine learning research featured in the conference incorporates concerns from various other topics such as Travelling salesman problem, Longest common subsequence problem and Diversity maintenance. In addition to Pattern recognition research, the conference aims to explore topics under Recurrent neural network, Image (mathematics), Human dynamics and Curse of dimensionality. The event facilitates discussions on Algorithm that incorporate concepts from other fields like Missing value imputation, Series (mathematics) and Survivability.

The most cited articles from the last conference are:

  • Misogynistic Tweet Detection: Modelling CNN with Small Datasets. (10 citations)
  • Community Aware Personalized Hashtag Recommendation in Social Networks (6 citations)
  • A Hybrid Missing Data Imputation Method for Constructing City Mobility Indices (6 citations)

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Research.com

The top authors publishing at Australasian Data Mining Conference (based on the number of publications) are:

  • Paul J. Kennedy (11 papers) absent at the last edition,
  • Peter Christen (9 papers) published 1 paper at the last edition,
  • Richi Nayak (7 papers) published 3 papers at the last edition, 1 more than at the previous edition,
  • Zahidul Islam (7 papers) published 1 paper at the last edition,
  • Andrew Stranieri (6 papers) absent at the last edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Research.com

Only papers with recognized affiliations are considered

The top affiliations publishing at Australasian Data Mining Conference (based on the number of publications) are:

  • Charles Sturt University (15 papers) published 7 papers at the last edition,
  • University of Technology, Sydney (15 papers) published 2 papers at the last edition,
  • University of Sydney (13 papers) published 4 papers at the last edition,
  • Federation University Australia (11 papers) absent at the last edition,
  • Deakin University (10 papers) absent at the last edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Research.com

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

Research.com

During the most recent 2018 edition, 3.33% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 58.62% were posted by at least one author from the top 10 institutions publishing at the conference. Another 13.79% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.14% of all publications and 3.45% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Research.com

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

Research.com

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

Research.com

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Research.com

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