Finding Fuzzy Close Frequent Itemsets from Databases
نویسندگان
چکیده
منابع مشابه
Finding Cyclic Frequent Itemsets
Mining various types of association rules from supermarket datasets is an important data mining problem. One similar problem involves finding frequent itemsets and then deriving rules from frequent itemsets. The supermarket data is temporal. Considering time attributes in the supermarket dataset some association rules can be extracted which may hold for a small time interval and not throughout ...
متن کاملMining Frequent Gradual Itemsets from Large Databases
Mining gradual rules plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form “The more/less X, then the more/less Y ”. Such rules have been studied since the early 70’s, mostly in the fuzzy logic ...
متن کاملFinding the True Frequent Itemsets
Frequent Itemsets (FIs) mining is a fundamental primitive in data mining that requires to identify all itemsets appearing in a fraction at least θ of a transactional dataset D. Often though, the ultimate goal of mining D is not an analysis of the dataset per se, but the understanding of the underlying process that generated D. Specifically, in many applications D is a collection of samples obta...
متن کاملMining Frequent Itemsets over Uncertain Databases
In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the fre...
متن کاملEfficiently Mining Frequent Itemsets in Transactional Databases
Discovering frequent itemsets is an essential task in association rules mining and it is considered to be computationally expensive. To find the frequent itemsets, the algorithm of frequent pattern growth (FP-growth) is one of the best algorithms for mining frequent patterns. However, many experimental results have shown that building conditional FP-trees during mining data using this FP-growth...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2018
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.10.257