کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382607 660772 2013 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Discovering diverse association rules from multidimensional schema
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Discovering diverse association rules from multidimensional schema
چکیده انگلیسی


• We propose a knowledge discovery methodology to discover diverse association rules.
• We utilize machine learning and statistical techniques for designing schema.
• We identify and rank the most informative dimensions present high dimensional schema.
• We extract informative data cubes at different levels of data abstraction.
• We perform case studies on three real-world datasets to validate our methodology.

The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 40, Issue 15, 1 November 2013, Pages 5975–5996
نویسندگان
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