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:16.20
Contributing Best Scientists:420
H5-index:
Papers published by Best Scientists682
Research Ranking (Computer Science)15
Research Ranking (Computer Science)12
Conference Call for Papers
Topics of interest include, but are not limited to:
Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, sampling, parallel and distributed data science (cloud, map-reduce, federated learning), novel algorithmic and statistical techniques for big data, data cleaning and preparation that uses learning, algorithmically efficient data transformation and integration.
Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, transfer learning, representation learning, meta learning, reinforcement learning; classification, clustering, regression, semi-supervised, self-supervised learning, few shot learning and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, ethics and robustness.
Overview
Top Research Topics at Knowledge Discovery and Data Mining?
Artificial intelligence (38.13%)
Data mining (33.46%)
Machine learning (25.62%)
The aim of Knowledge Discovery and Data Mining is to expand the discussion of research in Artificial intelligence, Data mining, Machine learning, Cluster analysis and Data science.
Natural language processing and Pattern recognition are some topics wherein Artificial intelligence research discussed in the conference have an impact.
Data mining research presented in Knowledge Discovery and Data Mining encompasses a variety of subjects, including Set (abstract data type) and Data set.
Discussions in the conference are anchored in the subject of Machine learning and the similar topic of Task (project management).
Research on Cluster analysis presented in it focuses, in particular, on Correlation clustering, CURE data clustering algorithm, Fuzzy clustering, Data stream clustering and Canopy clustering algorithm.
The majority of Correlation clustering studies presented zero in on Constrained clustering.
What are the most cited papers published at the conference?
A density-based algorithm for discovering clusters in large spatial Databases with Noise (13069 citations)
XGBoost: A Scalable Tree Boosting System (7651 citations)
Mining and summarizing customer reviews (5541 citations)
Research areas of the most cited articles at Knowledge Discovery and Data Mining:
The most cited articles mostly deal with topics like Data mining, Artificial intelligence, Machine learning, Cluster analysis and Pattern recognition.
While the published papers focused on Data mining, they were also able to explore topics like Set (abstract data type) and Data set.
The most cited papers hold forums on Artificial intelligence that merge themes from other disciplines such as Recommender system and Natural language processing.
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:
Knowledge Discovery and Data Mining is organized to address concerns in the fields of Artificial intelligence, Machine learning, Theoretical computer science, Deep learning and Graph (abstract data type).
Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Recommender system, Task (project management) and Pattern recognition.
The studies on Machine learning discussed can also contribute to research in the domains of Quality (business), Domain (software engineering), Inference and Benchmark (computing).
The studies in Theoretical computer science featured incorporate elements of Node (networking), Embedding, Structure (mathematical logic) and Feature learning.
The work tackled in the conference goes beyond the discipline of Deep learning as it also encompasses Data science.
The research on Graph (abstract data type) discussed in the conference draws on the closely related field of Data mining.
The most cited articles from the last conference are:
A Transformer-based Framework for Multivariate Time Series Representation Learning (6 citations)
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling (5 citations)
Socially-Aware Self-Supervised Tri-Training for Recommendation (5 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
Top authors and change over time
The top authors publishing at Knowledge Discovery and Data Mining (based on the number of publications) are:
Jiawei Han (121 papers) published 2 papers at the last edition, 6 less than at the previous edition,
Christos Faloutsos (90 papers) absent at the last edition,
Philip S. Yu (81 papers) published 4 papers at the last edition, 1 more than at the previous edition,
Hui Xiong (76 papers) published 11 papers at the last edition, 5 more than at the previous edition,
Jian Pei (56 papers) published 8 papers at the last edition, 3 more than at the previous 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
Top affiliations and change over time
Only papers with recognized affiliations are considered
The top affiliations publishing at Knowledge Discovery and Data Mining (based on the number of publications) are:
IBM (351 papers) published 18 papers at the last edition, 3 more than at the previous edition,
Microsoft (344 papers) published 35 papers at the last edition, 9 more than at the previous edition,
Carnegie Mellon University (247 papers) published 11 papers at the last edition, 2 more than at the previous edition,
Tsinghua University (235 papers) published 30 papers at the last edition, 3 more than at the previous edition,
University of Illinois at Urbana–Champaign (188 papers) published 19 papers at the last edition, 1 less than at the previous 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 2021 edition, 1.38% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.86% were posted by at least one author from the top 10 institutions publishing at the conference. Another 16.23% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.44% of all publications and 31.46% 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.
