| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1115 | 9 | 13 | 1 |
International Journal of Business Intelligence and Data Mining investigates studies in Data mining, Artificial intelligence, Cluster analysis, Machine learning and Pattern recognition. The journal facilitates discussions on Data mining that incorporate concepts from other fields like Information extraction and Fuzzy logic. The Artificial intelligence study featured in the journal draws connections with the study of Natural language processing.
International Journal of Business Intelligence and Data Mining focuses on Cluster analysis as well as the interrelated topic of Information retrieval. Research in the field of Data warehouse was used to conduct the presented Online analytical processing study. Research in Data warehouse tackled falls within the umbrella of Database.
The most cited papers generally zeroe in on subjects such as Data mining, Cluster analysis, Artificial intelligence, Information extraction and Association rule learning. While work presented in the published articles provide substantial information on Data mining, it also covers topics in Information retrieval and Data science. The published papers explore topics in Artificial intelligence which can be helpful for research in disciplines like Machine learning, Stochastic optimization and Pattern recognition.
International Journal of Business Intelligence and Data Mining mainly deals with areas of study such as Artificial intelligence, Machine learning, Data science, Data mining and Pattern recognition. International Journal of Business Intelligence and Data Mining addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Scalability, Series (mathematics) and Group (mathematics). Topics in Machine learning were tackled in line with various other fields like Question answering and Market segmentation.
It holds forums on Data science that merges themes from other disciplines such as Identification (information), Social media marketing, Workflow and Big data. International Journal of Business Intelligence and Data Mining links adjacent topics like Data mining with Mapping algorithm. Pattern recognition research featured in it incorporates concerns from various other topics such as Compact space and Fuzzy clustering.
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 International Journal of Business Intelligence and Data Mining (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 International Journal of Business Intelligence and Data Mining (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 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.
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.
This extensive research and detailed analysis driven coverage on Business Intelligence and Data Mining not only paves the way for extracting important information but also opens up an array of career opportunities. A career in fields like preschool teaching, for example, can significantly benefit from this knowledge, especially in the digital age. Potential job roles not confined to system analyst, data scientist, Artificial Intelligence specialist etc., can utilize these complex conceptions to affect practical scenarios in a constructive manner. In contexts such as teaching, teachers can employ data mining and AI systems to grasp student behavior, learning patterns, and areas of struggle, leading to more personalized curriculums. For aspiring teachers, these technologies can redefine their career prospects. They can leverage these cutting-edge technologies in preschool classrooms to contribute effectively to early childhood education. If you're interested and want to learn more about how these can be implemented, this comprehensive guide on [how to become a preschool teacher in Pennsylvania](https://research.com/careers/how-to-become-a-preschool-teacher-in-pennsylvania) provides an insightful understanding. Applications of these technologies are indeed diverse, but the unifying theme remains improving efficiency and understanding. Hence, a career in Business Intelligence and Data Mining is full of possibilities; it combines multi-disciplinary knowledge and innovation, making the world more connected and informed.
Kelly Cristina Ramos Da Silva;Helder Luiz Costa De Oliveira;Andre Ponce De Leon F. De Carvalho
(2021)Chemseddine Berbague;Hassina Seridi;Markus Zanker;Panagiotis Symeonidis
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