The resulting dataset has a size of about 1; 48GB. It contains exactly 1:692:082 transactions with 5:267:656 distinct items. The maximal length of a transaction is 71:472. Figure 1 plots the number of frequent itemsets as a function of the support threshold, while Figure 2 shows a bitmap representing the horizontal dataset, where items were sorted by their frequency. Note that to reduce the siz...

Mining frequent patterns in data is a useful requirement in several applications to guide future decisions. Association rule mining discovers interesting relationships among a large set of data items. Several association rule mining techniques exist, with the Apriori algorithm being common. Numerous algorithms have been proposed for efficient and fast association rule mining in data bases, but ...

Frequent Itemsets Mining(FIM) is a typical data mining task and has gained much attention. Due to the consideration of individual privacy, various studies have been focusing on privacy-preserving FIM problems. Differential privacy has emerged as a promising scheme for protecting individual privacy in data mining against adversaries with arbitrary background knowledge. In this paper, we present ...

AIM To determine the incidence of aspiration and ingestion of endodontic instruments in France during root canal treatment without using rubber dam. METHODOLOGY Data was provided by two insurance companies representing 24,651 French general dentists over 11 years. The type and number of accidents per year, the number of dental items involved and the percentage of occurrence of either aspirati...

In this paper, we investigate the problem of estimating the number of times data items that recur in very large distributed data streams. We present an alternative approach to the well-known CountMin Sketch in order to reduce the impact of collisions on the accuracy of the estimation. We propose to decrease, for each concerned item, the over-estimation that results from these collisions. Our sk...

The main goal of association rule mining is to discover relationships among sets of items in a transactional database. Association rule mining was introduced by Agrawal, Imielinski and Swami (1993). It aims to extract interesting correlations, frequent patterns, associations or causal structures among sets of items in transaction databases or other data repositories. The relationships are not b...

The input of a classical application of association mining is a large set of transactions, each consisting of a list of items a customer has paid for at a supermarket checkout desk. The goal is to identify groups of items (“itemsets”) that frequently co-occur in the same shopping carts. This paper focuses on an aspect that has so far received relatively little attention: the composition of the ...

In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster...

This study addresses the problem of discovering frequent items in unstructured P2P networks. This problem is relevant for several distributed services such as cache management, data replication, sensor networks and security. We make three contributions to the current state of the art. First, we propose a fully distributed Protocol for Frequent Item Set Discovery (ProFID) where the result is pro...

Mining class association rules is an important task for associative classification and plays a key role in rule-based decision support systems. Most of the existing methods try the best to mine rules with high reliability but ignore their capability for classifying potential objects. This paper defines a concept of -stronger relationship, and proposes a new method that integrates classification...