| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 166 | 79 | 69 | 30 |
The objective of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery is to combine knowledge in the areas of Artificial intelligence, Data mining, Data science, Machine learning and Cluster analysis. The studies on Artificial intelligence discussed can also contribute to research in the domains of Pattern recognition and Natural language processing. In the Data mining research discussed, Knowledge extraction, Association rule learning, Data stream mining and Decision tree are all tackled.
The journal focuses on Data science but the discussions also offer insight into other areas such as Taxonomy (general), Field (computer science), Key (cryptography) and Big data. Fuzzy clustering is part of Cluster analysis studies tackled in Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery. It connects research in Fuzzy clustering with the related topic of Correlation clustering.
Correlation clustering research presented is mostly focused on the subject of CURE data clustering algorithm.
The journal publications are organized to address concerns in the fields of Data mining, Artificial intelligence, Machine learning, Data science and Cluster analysis. While Data mining is the focus of the most cited articles, it also provides insights into the studies of Data set and Regression. The most cited articles focus on Data science but the discussions also offer insight into other areas such as Exploit, Context (language use), Scalability and Key (cryptography).
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery primarily tackles Artificial intelligence, Data science, Deep learning, Big data and Artificial neural network. The journal facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning and Pattern recognition. The concepts on Machine learning presented in the journal can also apply to other research fields, including Big data mining and Computational geometry.
The journal explores topics in Data science which can be helpful for research in disciplines like Data stream analysis and Privacy preserving. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery focuses on Deep learning but the discussions also offer insight into other areas such as Context (language use), Image (mathematics), Relation (database) and Taxonomy (general). Research in Analytics tackled falls within the umbrella of Data mining.
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 Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (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 Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (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, 4.65% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.20% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.88% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.07% of all publications and 65.85% 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.
With such advanced research and myriad information being published in the Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, numerous career opportunities in data science and related fields such as Artificial Intelligence, Machine Learning and Data Mining can be unlocked. Moreover, considering the global boom in technology, these fields are in high demand within multiple sectors, contributing to the vast expanse of career opportunities. Ranging from roles in academia to top-tier tech corporations, options for graduates and professionals are varied and high in number.
The journey of becoming a professional in these sectors requires substantial education and appropriate skill development. Similar to how you would follow the process of learning how to become a high school English teacher in Rhode Island, the route to becoming a data scientist or AI specialist also involves a dedicated period of studying and gaining work experience. Principally, individuals need a strong background in mathematics, statistics, or computer science, though the exact requirements can be quite flexible depending on the specifics of the job role. Subsequently, gaining real-world experience through internships or entry-level jobs to apply theoretical knowledge is crucial.
Reading and understanding research articles like those discussed in this article could provide an excellent knowledge base and inspiration for those interested in pursuing a career in data science-related sectors. Advancements and explorations within this field are monumental in the digital age, making data science a promising career path to consider.
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