Data Mining: Concepts and Techniques (2nd edition)

627 indexed citations
published 2006
Authors
Jiawei HanMicheline Kamber

In The Last Decade

doi.org/w87888775 →

Countries where authors are citing Data Mining: Concepts and Techniques (2nd edition)

Specialization
Citations

This map shows the geographic impact of Data Mining: Concepts and Techniques (2nd edition). It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Data Mining: Concepts and Techniques (2nd edition) with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Data Mining: Concepts and Techniques (2nd edition) more than expected).

Fields of papers citing Data Mining: Concepts and Techniques (2nd edition)

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Data Mining: Concepts and Techniques (2nd edition). Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Data Mining: Concepts and Techniques (2nd edition).

About Data Mining: Concepts and Techniques (2nd edition)

This paper, published in 2006, received 627 indexed citations . Written by Jiawei Han and Micheline Kamber covering the research area of Signal Processing, Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (310 citations), Information Systems (279 citations) and Signal Processing (106 citations).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w87888775.

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