ACM Transactions on Knowledge Discovery from Data

Top Research Topics at ACM Transactions on Knowledge Discovery From Data?

Artificial intelligence, Data mining, Machine learning, Cluster analysis and Theoretical computer science are among the topics commonly tackled in ACM Transactions on Knowledge Discovery From Data. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Natural language processing, Graph (abstract data type), Task (project management) and Pattern recognition. The study on Data mining presented in ACM Transactions on Knowledge Discovery From Data intersects with subjects under the field of Scalability.

The majority of Machine learning studies in ACM Transactions on Knowledge Discovery From Data are focused on the subject of Recommender system. Cluster analysis studies presented in ACM Transactions on Knowledge Discovery From Data focus on topics such as Correlation clustering and Fuzzy clustering. Constrained clustering and CURE data clustering algorithm are all areas of Correlation clustering tackled in the journal.

The work tackled in ACM Transactions on Knowledge Discovery From Data goes beyond the discipline of Anomaly detection as it also encompasses Outlier.

• Artificial intelligence (33.39%)
• Data mining (31.00%)
• Machine learning (20.67%)

What are the most cited papers published in the journal?

• L-diversity: Privacy beyond k-anonymity (2742 citations)
• Graph evolution: Densification and shrinking diameters (1775 citations)
• Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering (925 citations)

Research areas of the most cited articles at ACM Transactions on Knowledge Discovery From Data:

The journal articles investigate areas of study like Data mining, Artificial intelligence, Cluster analysis, Machine learning and Theoretical computer science. In particular, the Data mining works presented in the journal publications emphasize discussions on Anomaly detection. Most of the Artificial intelligence studies addressed in the published papers also intersect with Pattern recognition.

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 ACM Transactions on Knowledge Discovery From Data (based on the number of publications) are:

• Christos Faloutsos (17 papers) absent at the last edition,
• Philip S. Yu (13 papers) absent at the last edition,
• Zhiwen Yu (11 papers) absent at the last edition,
• Hui Xiong (10 papers) absent at the last edition,
• Bin Guo (10 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 ACM Transactions on Knowledge Discovery From Data (based on the number of publications) are:

• Carnegie Mellon University (27 papers) absent at the last edition,
• Tsinghua University (24 papers) absent at the last edition,
• Arizona State University (24 papers) absent at the last edition,
• University of Illinois at Chicago (16 papers) absent at the last edition,
• Microsoft (16 papers) absent 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 2022 edition, 100.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, nan% were posted by at least one author from the top 10 institutions publishing in the journal. Another nan% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included nan% of all publications and nan% 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.

Resource for Aspiring Researchers

Another important aspect potential researchers might want to consider is the practical applications in career paths. One such profession where the knowledge from these mentioned research topics can be applied is that of a preschool teacher. A preschool teacher needs a good grasp of Artificial Intelligence (AI), Machine Learning (ML), and data analysis, all subjects that are regularly covered by the ACM Transactions on Knowledge Discovery From Data. If you are considering a career in early childhood education, particularly in the state of Maine, we have a useful resource on how do you become a preschool teacher in Maine?. In this guide, you will find all the necessary steps to start your career path, with insights on how to incorporate your knowledge from subjects like AI and ML in a preschool setting. It's an excellent example of how cutting-edge research in AI and ML can be utilized in various sectors, including early childhood education.