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...

Nowadays high fuzzy utility based pattern mining is an emerging topic in data mining. It refers to discover all patterns having a high utility meeting a user-specified minimum high utility threshold. It comprises extracting patterns which are highly accessed in mobile web service sequences. Different from the traditional fuzzy approach, high fuzzy utility mining considers not only counts of mob...

Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. Based on PID_list, we propose a new algorithm called VME for mining erasable itemsets efficiently. The main advantage of VME algorithm is that...

In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent itemset mining process is represented as a data flow controlled by a series producer and consumer components (filters), and the data flow (communication) between such filters is made via streams. When production rate matches consup...

In this paper, we propose a parallel algorithm for mining maximal itemsets. We propose POP-MAX (Parallel Order Preserving MAXimal itemset algorithm), a fast and memory efficient parallel algorithm which enumerates all the maximal patterns concurrently and independently across several nodes. Also, POP-MAX uses an efficient maximality checking technique which determines the maximality of an items...

Closed frequent itemset mining plays an essential role in data stream mining. It could be used in business decisions, basket analysis, etc. Most methods for mining closed frequent itemsets store the streamlined information in compact data structure when data is generated. Whenever a query is submitted, it outputs all closed frequent itemsets. However, the online processing of existing approache...

Urban management is one of the most prominent problems in modern and contemporary social governance. With scale city, flow industries population becomes larger larger, city takes on more risks emergencies. In order to minimize losses prevent advance, early drills are often best way This paper aims use data mining technology design a smart emergency system achieve role warning time response even...

Recently there has been a growing interest in designing differentially private data mining algorithms. A variety of algorithms have been proposed for mining frequent itemsets. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. It has practical importance in a wide range of application areas such as decision support, web usage mining, bioinformatics, etc. In th...

Data items have been extracted using an empirical data mining technique called frequent itemset mining. In majority of theapplication contexts items are enriched with weights. Pushing an item weights into the itemset extraction process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient weighteditemset mining algorithms a...