دانلود مقاله ISI انگلیسی شماره 46038
عنوان فارسی مقاله

گزاره ای کردن قابل انعطاف ویژگی های مستمر در داده کاوی رابطه ای

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46038 2015 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Flexible propositionalization of continuous attributes in relational data mining
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 21, 30 November 2015, Pages 7698–7709

کلمات کلیدی
داده های رابطه ای و استخراج - ویژگی عددی - تجمع - کشف دانش
پیش نمایش مقاله
پیش نمایش مقاله گزاره ای کردن قابل انعطاف ویژگی های مستمر در داده کاوی رابطه ای

چکیده انگلیسی

In a relational database, data are stored in primary and secondary tables. Propositionalization can transform a relational database into a single attribute-value table, and hence becomes a useful technique for mining relational databases. However, most of the existing propositionalization approaches deal with categorical attributes, and cannot handle a threshold on an attribute and a threshold on the number of objects satisfying the condition on the attribute at the same time. In this paper, we propose a new propositionalization technique called Cardinalization to solve these problems. In order to handle relative numbers, we propose a second variant of our approach called Quantiles which can discretize the cardinality of Cardinalization and achieve a fixed number of features. Therefore, the Quantiles method can be tuned to different deployment contexts. Additionally, we often observe that the best combination of propositionalization and classification methods depends on the new context (e.g., online/incremental learning). One effective solution could be to predict the optimal combination at training time and use it in different deployment contexts. Here we also propose an effective wrapping algorithm, called WPC (Wrapper to combine Propositionalizer and Classifier) to select the best combination of propositionalization and classification methods to address this task. Extensive performance analyses in synthetic and real-life datasets show that our approach is very effective and efficient in relational data mining.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.