Data Mining and Knowledge Discovery

Top Research Topics at Data Mining and Knowledge Discovery?

Data Mining and Knowledge Discovery facilitates discussions on Data mining, Artificial intelligence, Machine learning, Cluster analysis and Pattern recognition. Data Mining and Knowledge Discovery explores research in Data mining and the adjacent study of Theoretical computer science. Research on Artificial intelligence addressed in the journal frequently intersections with the field of Series (mathematics).

The journal facilitated presentations on Cluster analysis research, particularly Correlation clustering, Fuzzy clustering, CURE data clustering algorithm, Constrained clustering and Canopy clustering algorithm. Data stream clustering and Single-linkage clustering are all aspects of Correlation clustering research featured in Data Mining and Knowledge Discovery. Data Mining and Knowledge Discovery connects research in Knowledge extraction with the related topic of Data science.

• Data mining (47.08%)
• Artificial intelligence (39.63%)
• Machine learning (27.97%)

What are the most cited papers published in the journal?

• A Tutorial on Support Vector Machines for Pattern Recognition (14699 citations)
• Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach (2114 citations)
• Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values (1678 citations)

Research areas of the most cited articles at Data Mining and Knowledge Discovery:

The published articles focus on Data mining, Artificial intelligence, Machine learning, Cluster analysis and Association rule learning. The published articles hold forums on Data mining that merge themes from other disciplines such as Representation (mathematics) and Outlier. The journal papers explore topics in Artificial intelligence which can be helpful for research in disciplines like Algorithm and Pattern recognition.

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

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

Data Mining and Knowledge Discovery investigates areas of study like Artificial intelligence, Machine learning, Data mining, Algorithm and Cluster analysis. The research on Artificial intelligence featured in Data Mining and Knowledge Discovery combines topics in other fields like Regression and Pattern recognition. In addition to Machine learning research, the journal aims to explore topics under Classifier (UML) and Benchmark (computing).

The studies in Data stream mining under the umbrella field of Data mining overlap with concepts in Reuse. It holds forums on Algorithm that merges themes from other disciplines such as Pairwise comparison, Nearest neighbor search, Pruning (decision trees) and Distance measures. Data Mining and Knowledge Discovery addresses concerns in Cluster analysis which are intertwined with other disciplines, such as Theoretical computer science, Key (cryptography) and Euclidean distance.

The most cited articles from the last journal are:

• A survey of community detection methods in multilayer networks (13 citations)
• Relational Learning Analysis of Social Politics using Knowledge Graph Embedding (6 citations)
• The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances (6 citations)

Papers citation over time

A key indicator for each journal 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.

Top authors and change over time

The top authors publishing in Data Mining and Knowledge Discovery (based on the number of publications) are:

• Eamonn Keogh (29 papers) published 1 paper at the last edition, 3 less than at the previous edition,
• Geoffrey I. Webb (14 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Nikolaj Tatti (14 papers) published 1 paper at the last edition,
• Aristides Gionis (13 papers) published 1 paper at the last edition, 1 less than at the previous edition,
• Jiawei Han (12 papers) absent at the last edition.

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

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Data Mining and Knowledge Discovery (based on the number of publications) are:

• IBM (40 papers) absent at the last edition,
• University of California, Riverside (32 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Monash University (19 papers) published 5 papers at the last edition, 1 more than at the previous edition,
• Katholieke Universiteit Leuven (19 papers) absent at the last edition,
• Microsoft (17 papers) published 1 paper at the last edition.

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

Publication chance based on affiliation

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

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.

During the most recent 2021 edition, 2.27% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.79% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.79% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.93% of all publications and 53.49% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal 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 journal in relation to all participants in a given year.

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.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, 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).

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

Career Opportunities in Data Mining and Knowledge Discovery

As the field of Data Mining and Knowledge Discovery expands, there has been a steady rise in job prospects. Many researchers who delve into this field go on to have successful careers in academia, private companies, or even start their own businesses. One potential career path can involve working in educational institutions, contributing to curriculums and inspiring the next generation to explore data mining and artificial intelligence. For instance, becoming a private school teacher particularly in New York City, one of the technology hubs of the world, can be a rewarding avenue to consider.

To make this career change, there are certain certification and educational requirements. Private schools, especially in New York, are known for their high academic standards, so prospective teachers need to showcase excellence in their chosen field. To better understand these prerequisites, you can refer to our article on private school teacher requirements in New York.

Whether you aspire to be an educator, remain in academia or branch out into the corporate world, the skills that you have honed in data mining and knowledge discovery will serve you greatly. Regardless of your path, the future prospects in this field are promising, spurred by ongoing advancements in technology and a growing demand for data analysis experts.