Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to m...

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...

1 Division of Data Science, Ton Duc Thang University, Ho Chi Minh, Viet Nam 4 2 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Viet Nam 5 [email protected], [email protected] 6 7 Abstract: In this paper, a method for mining frequent weighed closed itemsets (FWCIs) 8 from weighted item transaction databases is proposed. The motivation for FWCIs is that 9 frequent ...

Frequent itemset mining is a popular and important first step in the analysis of data arising in a broad range of applications. The traditional “exact” model for frequent itemsets requires that every item occurs in each supporting transaction. Real data is typically subject to noise and measurement error. To date, the effects of noise on exact frequent pattern mining algorithms have been addres...

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need differen...

Discovering maximal frequent itemset is a key issue in data mining; the Apriori-like algorithms use candidate itemsets generating/testing method, but this approach is highly time-consuming. To look for an algorithm that can avoid the generating of vast volume of candidate itemsets, nor the generating of frequent pattern tree, DCIP algorithm uses data-set condensing and intersection pruning to f...

We propose a novel approach for mining recent frequent itemsets. The approach has three key contributions. First, it is a single-scan algorithm which utilizes the special property of suffix-trees to guarantee that all frequent itemsets are mined. During the phase of itemset growth it is unnecessary to traverse the suffix-trees which are the data structure for storing the summary information of ...

A distributed algorithm based on Dynamic Itemset Counting (DIC) for generation of frequent itemsets is presented by us. DIC represents a paradigm shift from Apriori-based algorithms in the number of passes of the database hence reducing the total time taken to obtain the frequent itemsets. We exploit the advantage of Dynamic Itemset Counting in our algorithmthat of starting the counting of an i...

Technology is very influential in the world of increasingly fierce business competition so that people must find strategies to increase sales results midst competition. Ornamental plant sellers be smart managing stock and making selling ornamental plants. Transaction data can processed into information needed results, one which used as an analysis rules buyer transaction association purchasing ...

OBJECTIVE Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our ...