Statistical Analysis and Data Mining

Top Research Topics at Statistical Analysis and Data Mining?

The main points discussed in Statistical Analysis and Data Mining deals with Data mining, Artificial intelligence, Machine learning, Pattern recognition and Cluster analysis. The journal features works in Data mining, more specifically Anomaly detection, and explores their relation to disciplines like Statistical analysis. Statistical Analysis and Data Mining tackles issues in Artificial intelligence, particularly in the topics of Feature selection, Support vector machine, Classifier (UML) and Bayesian probability.

Cluster analysis research is the primary subject tackled in the journal with a focus on Correlation clustering.

• Data mining (40.74%)
• Artificial intelligence (31.25%)
• Machine learning (16.67%)

What are the most cited papers published in the journal?

• A survey on unsupervised outlier detection in high-dimensional numerical data (440 citations)
• A classification for community discovery methods in complex networks (262 citations)
• Relative clustering validity criteria: A comparative overview (194 citations)

Research areas of the most cited articles at Statistical Analysis and Data Mining:

The journal publications focus largely on the fields of Data mining, Artificial intelligence, Machine learning, Cluster analysis and Statistical analysis. The works on Data mining tackled in the published articles bring together disciplines like Missing data, Task (project management), Clustering high-dimensional data, Random forest and Feature selection. The studies on Artificial intelligence discussed at the most cited papers can also contribute to research in the domains of Test statistic and Pattern recognition.

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

• Statistics
• Artificial intelligence
• Machine learning

The previous edition focused in particular on these issues:

The journal focuses largely on the fields of Artificial intelligence, Bayesian probability, Pattern recognition, Data mining and Cluster analysis. In it, Inverse problem and Computer vision are investigated in conjunction with one another to address concerns in Artificial intelligence research. The research on Bayesian probability tackled can also make contributions to studies in the areas of Mixture model, Inference and Applied mathematics.

In addition to Pattern recognition research, Statistical Analysis and Data Mining aims to explore topics under Orientation (graph theory), Deep learning and Outlier. It facilitates the exploration of Data mining in relation to the field of Group structure. The concepts on Cluster analysis presented in Statistical Analysis and Data Mining can also apply to other research fields, including Modularity (networks), Node (networking), Structure (mathematical logic), Graphical model and Range (mathematics).

The most cited articles from the last journal are:

• Unsupervised random forests. (1 citations)
• A clustering method for graphical handwriting components and statistical writership analysis. (1 citations)
• Data-Driven Sparse Partial Least Squares (0 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 Statistical Analysis and Data Mining (based on the number of publications) are:

• Joseph S. Verducci (9 papers) absent at the last edition,
• Hillol Kargupta (7 papers) absent at the last edition,
• Kanishka Bhaduri (6 papers) absent at the last edition,
• Yufeng Liu (6 papers) absent at the last edition,
• Bertrand Clarke (6 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 Statistical Analysis and Data Mining (based on the number of publications) are:

• IBM (16 papers) absent at the last edition,
• Iowa State University (15 papers) published 2 papers at the last edition the same number as at the previous edition,
• Carnegie Mellon University (11 papers) absent at the last edition,
• University of Minnesota (10 papers) absent at the last edition,
• Columbia University (9 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 2021 edition, 9.09% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 40.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 20.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 10.00% of all publications and 30.00% 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 Statistical Analysis and Data Mining