scholarly journals A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yun Xue ◽  
Zhengling Liao ◽  
Meihang Li ◽  
Jie Luo ◽  
Qiuhua Kuang ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Heuristic Algorithms ◽  
Sequential Pattern ◽  
Microarray Data Analysis ◽  
New Approach ◽  
Dna Microarray Data ◽  
Marketing Systems ◽  
Common Subsequence ◽  
Synthetic Datasets ◽  
Frequent Sequential Pattern

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

scholarly journals A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach

2017 ◽  
Vol 6 (2) ◽  
pp. 20
Author(s):  
Kenmogne Edith Belise ◽  
Nkambou Roger ◽  
Tadmon Calvin ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Pattern Mining ◽  
Real Life ◽  
Growth Direction ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Large Field ◽  
Data Formats ◽  
Pattern Growth ◽  
Very Large Datasets ◽  
Synthetic Datasets

Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.

scholarly journals An Efficient Algorithm for Extracting High-Utility Hierarchical Sequential Patterns

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chunkai Zhang ◽  
Zilin Du ◽  
Yiwen Zu
Keyword(s):  
Pattern Mining ◽  
Search Space ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Second Phase ◽  
Two Phase ◽  
High Utility ◽  
Synthetic Datasets ◽  
Hierarchical Relation

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, where utility is used to measure the importance or weight of a sequence. However, the underlying informative knowledge of hierarchical relation between different items is ignored in HUSPM, which makes HUSPM unable to extract more interesting patterns. In this paper, we incorporate the hierarchical relation of items into HUSPM and propose a two-phase algorithm MHUH, the first algorithm for high-utility hierarchical sequential pattern mining (HUHSPM). In the first phase named Extension, we use the existing algorithm FHUSpan which we proposed earlier to efficiently mine the general high-utility sequences (g-sequences); in the second phase named Replacement, we mine the special high-utility sequences with the hierarchical relation (s-sequences) as high-utility hierarchical sequential patterns from g-sequences. For further improvements of efficiency, MHUH takes several strategies such as Reduction, FGS, and PBS and a novel upper bounder TSWU, which will be able to greatly reduce the search space. Substantial experiments were conducted on both real and synthetic datasets to assess the performance of the two-phase algorithm MHUH in terms of runtime, number of patterns, and scalability. Conclusion can be drawn from the experiment that MHUH extracts more interesting patterns with underlying informative knowledge efficiently in HUHSPM.

Sign in / Sign up

Export Citation Format

Share Document