A Selective Analysis of Microarray Data using Association Rule Mining

被引：16

作者：

Alagukumar, S. ^{[1
]}

Lawrance, R. ^{[2
]}

机构：

[1] Ayya Nadar Janaki Ammal Coll, Comp Sci, Sivakasi 626124, Tamil Nadu, India

[2] Ayya Nadar Janaki Ammal Coll, Dept Comp Applicat, Sivakasi 626124, Tamil Nadu, India

来源：

GRAPH ALGORITHMS, HIGH PERFORMANCE IMPLEMENTATIONS AND ITS APPLICATIONS (ICGHIA 2014) | 2015年 / 47卷

关键词：

Data mining; Microarray gene expression data; frequent pattern mining; gene association rules; gene expression analysis;

D O I：

10.1016/j.procs.2015.03.177

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Association analysis plays the vital role in the computational biology. DNA Microarrays allow for the simultaneously monitor of expression levels for thousands of genes or entire genomes. Microarray gene association analysis is exposing the biological relevant association between different genes under different experimental samples. Mining association rules has been applied successfully in various types of data for determining interesting association pattern. Frequent pattern mining is becoming a potential approach in microarray gene expression analysis. In this paper the most relevant mining association rules as well as main issues when discovering efficient and practical method for microarray gene association analysis have been reviewed. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：3 / 12

页数：10

共 50 条

[1] Boolean Association Rule Mining on Microarray Gene Expression Data
Vengateshkumar, R.
Alagukumar, S.
Lawrance, R.
[J]. ADVANCED COMPUTING AND INTELLIGENT ENGINEERING, 2020, 1082 : 99 - 111
[2] Web Data Analysis Using Negative Association Rule Mining
Kumar, Raghvendra
Pattnaik, Prasant Kumar
Sharma, Yogesh
[J]. INFORMATION SYSTEMS DESIGN AND INTELLIGENT APPLICATIONS, VOL 1, INDIA 2016, 2016, 433 : 513 - 518
[3] Using Dynamic Data Mining in Association Rule Mining
Qaddoum, Kifaya
[J]. MESM '2006: 9TH MIDDLE EASTERN SIMULATION MULTICONFERENCE, 2008, : 89 - 92
[4] An Analysis of the Survivability in SEER Breast Cancer Data Using Association Rule Mining
Li, Fangfang
Duan, Yu
[J]. SECURITY, PRIVACY AND ANONYMITY IN COMPUTATION, COMMUNICATION AND STORAGE, (SPACCS 2016), 2016, 0067 : 184 - 194
[5] Data- Driven Analysis of Stimulation Treatments Using Association Rule Mining
Ahmadi, Rouholah
Aminshahidy, Babak
Shahrabi, Jamal
[J]. SPE PRODUCTION & OPERATIONS, 2023, 38 (03): : 552 - 564
[6] Data-Driven Analysis of Stimulation Treatments Using Association Rule Mining
Ahmadi, Rouholah
Aminshahidy, Babak
Shahrabi, Jamal
[J]. SPE Production and Operations, 2023, 38 (03): : 552 - 564
[7] Incremental association rule mining using materialized data mining views
Morzy, M
Morzy, T
Królikowski, Z
[J]. ADVANCES IN INFORMATION SYSTEMS, PROCEEDINGS, 2004, 3261 : 77 - 87
[8] An extended association rule mining strategy for gene relationship discovery from microarray data
Peng, Bin
Zhu, Dianwen
Yang, Xiaowei
Liu, Ling
Huang, Wenquan
Zhou, Xiaohua
Yi, Dong
[J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2014, 84 (02) : 384 - 396
[9] Material Selection: Material Perception Data Analysis Using Clustering Analysis and Association Rule Analysis of Data Mining
Choi, Jaeho
[J]. KOREAN JOURNAL OF METALS AND MATERIALS, 2024, 62 (01): : 65 - 72
[10] Spatial data analysis using association rule mining in distributed environments: a privacy prospect
Kumar R.
Son L.H.
Jha S.
Mittal M.
Goyal L.M.
[J]. Spatial Information Research, 2018, 26 (6) : 629 - 638

← 1 2 3 4 5 →