Write in a clean editor, then format for Statistical Analysis and Data Mining The ASA Data Science Journal in one click — DocuGuru applies the official Wiley template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Statistical Analysis and Data Mining The ASA Data Science Journal is a peer-reviewed journal published by Wiley, covering Statistical Methods and Inference, Advanced Statistical Methods and Models, Bayesian Methods and Mixture Models.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Statistical Analysis and Data Mining The ASA Data Science Journal 12 (3): 45–58.
Formats any DOI in Statistical Analysis and Data Mining The ASA Data Science Journal style. No sign-up. |
| Publishes research in | Statistical Methods and Inference Advanced Statistical Methods and Models Bayesian Methods and Mixture Models Data Mining Algorithms and Applications Advanced Clustering Algorithms Research |
| ISSN | 1932-1864 |
| Citation impact (2-yr) | 0.84 |
| h-index | 50 |
| i10-index | 247 |
| Total citations | 12,734 |
| Article processing charge | $3,760 |
| Top institutions publishing here | Iowa State University |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract High‐dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term ‘curse of dimensionality’, more concrete aspects being the so‐called ‘distance concentration effect’, the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task…
Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are many different RF imputation algorithms,…
Abstract It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is , where is the number…
Abstract Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes…
Abstract Many real‐world networks are intimately organized according to a community structure. Much research effort has been devoted to develop methods and algorithms that can efficiently highlight this hidden structure of a network, yielding a vast literature on what is called today community detection . Since network representation can be very complex and can contain…
