| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 774 | 17 | 16 | 6 |
The journal aims to foster the development of research in Pattern recognition (psychology), Cluster analysis, Algorithm, Artificial intelligence and Statistics. In the journal, Similarity (network science), Multidimensional scaling and Psychometrics are investigated in conjunction with one another to address concerns in Pattern recognition (psychology) research. It addresses concerns in Cluster analysis which are intertwined with other disciplines, such as Data mining and Combinatorics.
Combinatorics and Ultrametric space are closely related fields of research discussed in the journal. The Algorithm works featured in the journal incorporate elements from k-means clustering and Mathematical optimization. The studies on Artificial intelligence discussed can also contribute to research in the domains of Natural language processing, Machine learning and Pattern recognition.
The journal is focused mainly on Pattern recognition, particularly Classifier (UML). Research on Statistics addressed in it frequently intersections with the field of Econometrics. The journal primarily discusses Correlation clustering topics, particularly CURE data clustering algorithm, Determining the number of clusters in a data set, Single-linkage clustering and Canopy clustering algorithm.
The journal publications are mainly concerned with subjects like Cluster analysis, Statistics, Pattern recognition (psychology), Combinatorics and Algorithm. The journal publications explore issues in Cluster analysis which can be linked to other research areas like Data mining and Pattern recognition. The most cited papers explore research in Econometrics and overlapping concepts in Linear regression to expand the discourse in Statistics.
The aim of the journal is to expand the discussion of research in Pattern recognition (psychology), Cluster analysis, Artificial intelligence, Algorithm and Data mining. Matrix (mathematics), Decision tree learning, Interpretability, Multivariate statistics and Applied mathematics are some topics wherein Pattern recognition (psychology) research discussed in the journal have an impact. The work on Cluster analysis tackled in the journal brings together disciplines like Mixture model, Similarity (network science), Projection (set theory) and Dimensionality reduction.
The studies in Artificial intelligence featured incorporate elements of Natural language processing, Machine learning and Pattern recognition. The featured Algorithm works encompass concepts such as Estimation theory and examines them in conjunction with Initialization. Journal of Classification addresses concerns in the field of Data mining by exploring it in line with topics in Consumer behaviour which intersect with Statistical model, Market segmentation, Hypersphere and Segmentation subjects.
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.
The top authors publishing in Journal of Classification (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in Journal of Classification (based on the number of publications) are:
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.
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, 5.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 21.05% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.89% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.53% of all publications and 60.53% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Understanding the theoretical concepts, methodologies, and findings presented in this journal is essential. Yet, equally important is the ability to translate these academic advancements into practical applications and foresee their potential future implications. This is particularly true for educators who need to stay updated with the latest research to adapt their teaching methods and curriculum accordingly. For instance, the journal's focus on Pattern recognition, Algorithm and Artificial Intelligence can be applied within the realm of education, especially in the development and improvement of teaching methods. With the evolution of digital learning, educators can make use of machine learning algorithms and pattern recognition to improve student learning experiences. Similarly, the journal's exploration of cluster analysis can guide educators to group students based on their learning patterns or abilities, thereby optimizing the learning process. Educators interested in leveraging such research insights can consider enrolling in a program dedicated to modern teaching methodologies. For example, many teaching credential programs have begun to integrate these cutting-edge research findings into their curriculum to equip teachers with the latest knowledge and skills. Among those, the {anchor} is known to offer training that is both affordable and comprehensive, consistently ranking among the best teaching credential programs in Georgia. Looking forward, the research topics discussed in this journal can be expected to pave the way for new educational technologies and teaching techniques, and influence the direction of educational policies. It's exciting to envision a future where academic research substantially contributes to the advancement of real-world pedagogical practices.
Michael C. Thrun;Alfred Ultsch
(2021)Gehad Ismail Sayed;Ashraf Darwish;Aboul Ella Hassanien
(2020)Antonella Plaia;Simona Buscemi;Johannes Fürnkranz;Eneldo Loza Mencía
(2021)Alberto Fernández;Sergio Gómez
(2020)Rainer Dangl;Friedrich Leisch
(2020)Youness Aliyari Ghassabeh;Frank Rudzicz
(2021)Tong Wu;Yanni Xiao;Muhan Guo;Feiping Nie
(2020)Nicole Ellenbach;Anne-Laure Boulesteix;Bernd Bischl;Kristian Unger
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