کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
379409 | 659299 | 2007 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A k-mean clustering algorithm for mixed numeric and categorical data
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A k-mean clustering algorithm for mixed numeric and categorical data A k-mean clustering algorithm for mixed numeric and categorical data](/preview/png/379409.png)
چکیده انگلیسی
Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 63, Issue 2, November 2007, Pages 503–527
Journal: Data & Knowledge Engineering - Volume 63, Issue 2, November 2007, Pages 503–527
نویسندگان
Amir Ahmad, Lipika Dey,