Discovering Interesting Association Rules by Clustering
نویسندگان
چکیده
There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm.
منابع مشابه
Developing a Course Recommender by Combining Clustering and Fuzzy Association Rules
Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate courses. The model uses clustering to identify students with similar interests and skills...
متن کاملDiscovering Interesting Association Rules in the Web Log Usage Data
The immense volume of web usage data that exists on web servers contains potentially valuable information about the behavior of website visitors. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. In this paper we will focus on applying association rules as a data mining technique to extract potentia...
متن کاملOn the Discovery of Interesting Patterns in Association Rules
Many decision support systems, which utilize association rules for discovering interesting patterns, require the discovery of association rules that vary over time. Such rules describe complicated temporal patterns such as events that occur on the “first working day of every month.” In this paper, we study the problem of discovering how association rules vary over time. In particular, we introd...
متن کاملVisual Grouping of Association Rules by Clustering Conditional Probabilities for Categorical Data
We demonstrate the use of a visual data-mining tool for non-technical domain experts within organizations to facilitate the extraction of meaningful information and knowledge from in-house databases. The tool is mainly based on the basic notion of grouping association rules. Association rules are useful in discovering items that are frequently found together. However in many applications, rules...
متن کاملAn Efficient Algorithm to Automated Discovery of Interesting Positive and Negative Association Rules
Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the discovering frequent items and the mining of positive rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. But these algorithms do not consider negation occurrence of the attribute i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004