On Minimal Infrequent Itemset Mining
نویسندگان
چکیده
A new algorithm for minimal infrequent itemset mining is presented. Potential applications of finding infrequent itemsets include statistical disclosure risk assessment, bioinformatics, and fraud detection. This is the first algorithm designed specifically for finding these rare itemsets. Many itemset properties used implicitly in the algorithm are proved. The problem is shown to be NP-complete. Experimental results are then presented.
منابع مشابه
A Survey on Infrequent Weighted Itemset Mining Approaches
Association Rule Mining (ARM) is one of the most popular data mining technique. All existing work is based on frequent itemset. Frequent itemset find application in number of real-life contexts e.g., market basket analysis, medical image processing, biological data analysis. In recent years, the attention of researchers has been focused on infrequent itemset mining. This paper tackles the issue...
متن کاملA Survey on Moving Towards Frequent Pattern Growth for Infrequent Weighted Itemset Mining
Data Mining and knowledge discovery is one of the important areas. In this paper we are presenting a survey on various methods for frequent pattern mining. From the past decade, frequent pattern mining plays a very important role but it does not consider the weight factor or value of the items. The very first and basic technique to find the correlation of data is Association Rule Mining. In ARM...
متن کاملMinimally Infrequent Itemset Mining using Pattern-Growth Paradigm and Residual Trees
Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth p...
متن کاملA Survey of Frequent and Infrequent Weighted Itemset Mining Approaches
Itemset mining is a data mining method extensively used for learning important correlations among data. Initially itemsets mining was made on discovering frequent itemsets. Frequent weighted item set characterizes data in which items may weight differently through frequent correlations in data’s. But, in some situations, for instance certain cost functions need to be minimized for determining r...
متن کاملImplementation of Efficient Algorithm for Mining High Utility Itemsets in Distributed and Dynamic Database
Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing items from the given database. High Utility Pattern Mining has become the recent research with respect to data mining. The proposed work is High Utility Pattern for distributed and dynamic database. The traditional method of mining frequent itemset mining embrace that the data is astride and sedent...
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 2007