Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study, we propose…
ACM SIGMOD Record Template
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About the ACM SIGMOD Record format
ACM SIGMOD Record is a peer-reviewed journal published by ACM, covering Advanced Database Systems and Queries, Data Management and Algorithms, Semantic Web and Ontologies.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM SIGMOD Record 12, 3, Article 45 (2023).
Formats any DOI in ACM SIGMOD Record style. No sign-up. |
| Publishes research in | Advanced Database Systems and Queries Data Management and Algorithms Semantic Web and Ontologies Distributed systems and fault tolerance Advanced Data Storage Technologies |
| ISSN | 0163-5808 |
| Citation impact (2-yr) | 0.96 |
| h-index | 173 |
| i10-index | 1,348 |
| Total citations | 158,595 |
| Top institutions publishing here | University of Wisconsin–Madison |
| Journal website | sigmodrecord.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in ACM SIGMOD Record per year
Citation impact of ACM SIGMOD Record by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in ACM SIGMOD Record
For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign…
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this…
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms…