Mining Frequent Itemsets with Normalized Weight in Continuous Data Streams

Abstract

Abstract-A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

Optimizing network traffic by generating association rules using hybrid apriori-genetic algorithm

“…An efficient algorithm Weighted Support Frequent Itemsets (WSFI) was used to mine with normalized weight over data streams [10]. They use a new tree structure known as Weighted Support FP-Tree (WSFP-Tree) for storing crucial information in compressed form about frequent itemsets.…”

Noval Stream Data Mining Framework under the Background of Big Data

“…In reference [10], linear linked list structure is used to store the current candidate set and transaction information of windows, and multi-thread method was used to generate frequent pattern. From the perspective of practical application, frequent items set mining problems of WSN and high speed network flow are studied in [13] of 2013 and [14] of 2014 and it achieved good results.…”

Extracting the Frequent Item Sets by Using Greedy Strategy in Hadoop

“…A WSFP-Tree is an information structure in light of an expanded FP-tree. It serves to store compacted urgent data about incessant examples [11]. A Survey on Algorithms for Mining Frequent Itemsets over Data Streams was exhibited by James Cheng et al [12] in year 2008. review various agent stateof-the-craftsmanship calculations on mining incessant itemsets, visit maximal itemsets, or successive shut itemsets over information streams.…”

“…Something else, if the predefined least support is exorbitantly low, at that point gathering blast may emerge. In 2010 Younghee Kim et al [11] gave Mining Frequent Itemsets Normalized Weight in Continuous Data Streams. They consider the issue of mining with weighted support over an information stream sliding window utilizing restricted memory space, called WSFI-Mine.…”