Applying data mining techniques for spatial distribution analysis of plant species co-occurrences

被引:7
作者
Estevao Silva, Luis Alexandre [1 ]
Siqueira, Marinez Ferreira [1 ]
Pinto, Flavia dos Santos [1 ]
Banos, Felipe Sodre M. [1 ]
Zimbrao, Geraldo [2 ]
Souza, Jano Moreira [2 ]
机构
[1] Inst Pesquisas Jardim Bot Rio de Janeiro, Rio De Janeiro, Brazil
[2] Univ Fed Rio de Janeiro, COPPE, BR-21941 Rio De Janeiro, Brazil
基金
美国国家科学基金会; 美国安德鲁·梅隆基金会;
关键词
Data analysis; Data mining; Association rules; Knowledge management applications; Knowledge discovery; POINT PATTERN-ANALYSIS; TREE DIVERSITY; FOREST; MODELS; ASSOCIATIONS; INVENTORY; KNOWLEDGE; GROWTH;
D O I
10.1016/j.eswa.2015.08.031
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The continuous growth of biodiversity databases has led to a search for techniques that can assist researchers. This paper presents a method for the analysis of occurrences of pairs and groups of species that aims to identify patterns in co-occurrences through the application of association rules of data mining. We propose, implement and evaluate a tool to help ecologists formulate and validate hypotheses regarding co-occurrence between two or more species. To validate our approach, we analyzed the occurrence of species with a dataset from the 50-ha Forest Dynamics Project on Barro Colorado Island (BCI). Three case studies were developed based on this tropical forest to evaluate patterns of positive and negative correlation. Our tool can be used to point co-occurrence in a multi-scale form and for multi-species, simultaneously, accelerating the identification process for the Spatial Point Pattern Analysis. This paper demonstrates that data mining, which has been used successfully in applications such as business and consumer profile analysis, can be a useful resource in ecology. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:250 / 260
页数:11
相关论文
共 68 条
[1]   Phishing detection based Associative Classification data mining [J].
Abdelhamid, Neda ;
Ayesh, Aladdin ;
Thabtah, Fadi .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (13) :5948-5959
[2]   Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].
Adomavicius, G ;
Tuzhilin, A .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2005, 17 (06) :734-749
[3]  
Aggarwal C.C., 2010, P SIAM INT C DATA MI, P478
[4]  
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
[5]  
[Anonymous], 2015, Data mining: the textbook
[6]  
[Anonymous], 2012, R LANG ENV STAT COMP
[7]  
[Anonymous], 1998, TROPICAL FOREST CENS
[8]   spatstat: An R package for analyzing spatial point patterns [J].
Baddeley, A ;
Turner, R .
JOURNAL OF STATISTICAL SOFTWARE, 2005, 12 (06) :1-42
[9]   Do community-level models describe community variation effectively? [J].
Baselga, Andres ;
Araujo, Miguel B. .
JOURNAL OF BIOGEOGRAPHY, 2010, 37 (10) :1842-1850
[10]  
Besag J. E., 1977, J ROYAL STAT SOC B, V39, P193, DOI DOI 10.1111/J.2517-6161.1977.TB01616.X