Mining Approximate Frequent Itemsets In the Presence of Noise: Algorithm and Analysis

Abstract

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 occur in each supporting transaction. However, real data is typically subject to noise and measurement error. To date, the effect of noise on exact frequent pattern mining algorithms have been addressed primarily through simulation studies, and there has been limited attention to the development of noise tolerant algorithms.In this paper we propose a noise tolerant itemset model, which we call approximate frequent itemsets (AFI). Like frequent itemsets, the AFI model requires that an itemset has a minimum number of supporting transactions. However, the AFI model tolerates a controlled fraction of errors in each item and each supporting transaction. Motivating this model are theoretical results (and a supporting simulation study presented here) which state that, in the presence of even low levels of noise, large frequent itemsets are broken into fragments of logarithmic size; thus the itemsets cannot be recovered by a routine application of frequent itemset mining. By contrast, we provide theoretical results showing that the AFI criterion is well suited to recovery of block structures subject to noise.We developed and implemented an algorithm to mine AFIs that generalizes the level-wise enumeration of frequent itemsets by allowing noise. We propose the noise-tolerant support threshold, a relaxed version of support, which varies with the length of the itemset and the noise threshold. We exhibit an Apriori property that permits the pruning of an itemset if any of its sub-itemset is not sufficiently supported. Several experiments presented demonstrate that the AFI algorithm enables better recoverability of frequent patterns under noisy conditions than existing frequent itemset mining approaches. Noise-tolerant support pruning also renders an order of magnitude performance gain over existing methods.

Mining Noise-Tolerant Frequent Closed Itemsets in Very Large Database

“…Jouni K. et al [8] proposed to mine the dense itemsets in the presence of noise where the dense itemsets are the itemsets with a sufficiently large sub-matrix that exceeds a given density threshold of attributes present. Liu et al [9] developed a general model for mining approximate frequent itemsets which controls errors of two directions in matrices formed by transactions and items. Selim et al [10] proposed an algorithm that is obtained by modifying a hierarchical agglomerative clustering algorithm and takes advantage of the speed that bit operations afford.…”

“…We say that the sub-matrix (I × T ) satisfies c condition if † Although [9] proposed an approximate Apriori property. However, it's not efficient enough, the search space is till very large.…”

“…In this section, we compare the quality of the output of NFCIM with another state-of-art noise tolerant algorithm, AFI [9]. The synthetic data T10.I4.D20K is treated as the dataset without any noise, denoted as R. The noise is introduced by flipping each element in the dataset with probability p. The dataset with noise is denoted as R n .…”

“…These small fragments are similar to each other and they would be redundant information once the large frequent itemsets were found. The second problem is addressed by the approach termed AFI, which is proposed by [9]. AFI could identify and fix the noises in the datasets.…”

Towards a temporal modeling of the genetic network controlling Systemic Acquired Resistance in Arabidopsis thaliana

“…Frequent itemset mining [22]- [23] is a key technique for the analysis of binary matrices. In the binary representation, a frequent itemset corresponds to a submatrix of 1s containing a sufficiently large set of rows.…”