A Parallel Mining Algorithm for Closed Sequential Patterns

2007 ◽  
Author(s):  
Tian Zhu ◽  
Sixue Bai
Keyword(s):  
Sequential Patterns ◽  
Mining Algorithm ◽  
Parallel Mining

scholarly journals An Efficient Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets in IoT

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 79162-79173 ◽  
Author(s):  
Zhang Tianrui ◽  
Wei Mingqi ◽  
Liu Bin
Keyword(s):  
Large Scale ◽  
Mining Algorithm ◽  
Parallel Mining

scholarly journals PARALLEL MINING OF LARGE MAXIMAL BICLIQUES USING ORDER PRESERVING GENERATORS

2014 ◽  
pp. 105-113 ◽  
Author(s):  
R. V. Nataraj ◽  
S. Selvan
Keyword(s):  
Parallel Algorithm ◽  
Optimization Techniques ◽  
Main Memory ◽  
Memory Consumption ◽  
Running Time ◽  
Mining Algorithm ◽  
Parallel Mining ◽  
Mining Algorithms ◽  
Symmetric Property ◽  
Memory Efficient

In this paper, we propose a parallel algorithm for mining large maximal bicliques from graph datasets. We propose POP-MBC (Parallel Order Preserving Maximal BiClique mining algorithm), a fast and memory efficient parallel algorithm, which enumerates all the maximal bicliques independently and concurrently across several processors without any synchronization between the processors. The POP-MBC algorithm is highly memory efficient since it does not store the previously computed patterns in the main memory and requires only the dataset to be stored in the memory. To enhance the load sharing among different nodes, POP-MBC uses a round robin strategy which enables to achieve load balancing as high as 90%. We have also incorporated bit-vectors and numerous optimization techniques exploiting the symmetric property of the graph dataset to reduce the memory consumption and overall running time of the algorithm. Our comp rehensive experimental analyses involving publicly available datasets show that our algorithm distributes the load among the different processors equally and takes less memory, less running time than other maximal biclique mining algorithms.

scholarly journals Dynamic Parallel Mining Algorithm of Association Rules Based on Interval Concept Lattice

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 647
Author(s):  
Yafeng Yang ◽  
Ru Zhang ◽  
Baoxiang Liu
Keyword(s):  
Association Rules ◽  
Time Complexity ◽  
Concept Lattice ◽  
Massive Data ◽  
Minimal Cost ◽  
Classical Concept ◽  
Mining Algorithm ◽  
Parallel Mining ◽  
Expansion Form

An interval concept lattice is an expansion form of a classical concept lattice and a rough concept lattice. It is a conceptual hierarchy consisting of a set of objects with a certain number or proportion of intent attributes. Interval concept lattices refine the proportion of intent containing extent to get a certain degree of object set, and then mine association rules, so as to achieve minimal cost and maximal return. Faced with massive data, the structure of an interval concept lattice is more complex. Even if the lattice structures have been united first, the time complexity of mining interval association rules is higher. In this paper, the principle of mining association rules with parameters is studied, and the principle of a vertical union algorithm of interval association rules is proposed. On this basis, a dynamic mining algorithm of interval association rules is designed to achieve rule aggregation and maintain the diversity of interval association rules. Finally, the rationality and efficiency of the algorithm are verified by a case study.

Campus Data Analysis Based on Positive and Negative Sequential Patterns

2019 ◽  
Vol 33 (05) ◽  
pp. 1959016 ◽  
Author(s):  
Xiaoqi Jiang ◽  
Tiantian Xu ◽  
Xiangjun Dong
Keyword(s):  
College Students ◽  
Time Series ◽  
Data Analysis ◽  
Sequential Patterns ◽  
Negative Events ◽  
Positive Events ◽  
Mining Algorithm ◽  
Consumption Data ◽  
Academic Grades ◽  
The Relationship

Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.

The Sequential Pattern Mining Algorithm MHSP Based on MH

2011 ◽  
Vol 63-64 ◽  
pp. 425-430
Author(s):  
Jun Wang ◽  
Ya Qiong Jiang
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Experimental Results ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Important Method ◽  
The Real ◽  
Mining Algorithm ◽  
Large Projection ◽  
Growth Approach

Pattern growth approach is an important method in sequential pattern mining. Projection database based on the method is introduced in PrefixSpan, and the PrefixSpan algorithm can solve the problem of mining sequential patterns. But relative to large projection database, the performance of PrefixSpan is affected. Inspired by the prefix-divide method and MH structure, this paper proposed a new algorithm MHSP for sequential pattern mining. Based on the real datasets, experimental results show that the performance of MHSP algorithm is more than twice as fast as PrefixSpan.

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.

Sign in / Sign up

Export Citation Format

Share Document