An Efficient Approach for Mining Fault-Tolerant Frequent Patterns Based on Bit Vector Representations
نویسندگان
چکیده
In this paper, an algorithm, called VB-FT-Mine (Vectors-Based Fault–Tolerant frequent patterns Mining), is proposed for mining fault-tolerant frequent patterns efficiently. In this approach, fault–tolerant appearing vectors are designed to represent the distribution that the candidate patterns contained in data sets with fault-tolerance. VB-FT-Mine algorithm applies depth-first pattern growing method to generate candidate patterns. The fault-tolerant appearing vectors of candidates are obtained systematically, and the algorithm decides whether a candidate is a fault-tolerant frequent pattern quickly by performing vector operations on bit vectors. The experimental results show that VB-FT-Mine algorithm has better performance on execution time significantly than FT-Apriori algorithm proposed previously.
منابع مشابه
An Efficient Approach for Mining Top-K Fault-Tolerant Repeating Patterns
In this paper, an efficient strategy for mining top-K non-trivial faulttolerant repeating patterns (FT-RPs in short) with lengths no less than min_len from data sequences is provided. By extending the idea of appearing bit sequences, fault-tolerant appearing bit sequences are defined to represent the locations where candidate patterns appear in a data sequence with insertion/deletion errors bei...
متن کاملHigh Fuzzy Utility Based Frequent Patterns Mining Approach for Mobile Web Services Sequences
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...
متن کاملRamp: High Performance Frequent Itemset Mining with Efficient Bit-Vector Projection Technique
Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp (Real Algorithm...
متن کاملRamp: Fast Frequent Itemset Mining with Efficient Bit-Vector Projection Technique
Mining frequent itemset using bit-vector representation approach is very efficient for dense type datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. To check the efficiency of our bit-vector projection technique, we present a ...
متن کاملMax-FTP: Mining Maximal Fault-Tolerant Frequent Patterns from Databases
Mining Fault-Tolerant (FT) Frequent Patterns in real world (dirty) databases is considered to be a fruitful direction for future data mining research. In last couple of years a number of different algorithms have been proposed on the basis of Apriori-FT frequent pattern mining concept. The main limitation of these existing FT frequent pattern mining algorithms is that, they try to find all FT f...
متن کامل