Computer Programs for Hierarchical Polythetic Classification ("Similarity Analyses")

Lance, G. N.; Williams, W. T.

doi:10.1093/comjnl/9.1.60

Cited by 332 publications

(135 citation statements)

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“…3. Top five diversity indicators with the highest correlation: with individual qualities of timber production (a), with individual age categories (b) with the study area QTP -indicator of the quality of timber production, QTP1 -best-quality timber, QTP2, QTP3 -sawn timber of higher and lower quality, QTP4 -pulp and fuel wood, black circle -percentage of significant correlations of the diversity indicator with the quality of timber production, grey bar -average value of correlation coefficient R xy calculated from significant correlations of a particular diversity indicator, white rectangle -95% confidence interval of the average value of correlation coefficient R xy calculated from all correlations derived for a particular diversity indicator, black dagger -average value of correlation coefficient R xy calculated from all correlations derived for a particular diversity indicator, black diamond -structural indicator of diversity, BC1_SP_Rr -BC1 index of similarity (Bray, Curtis 1957) between the trees with diameter below 7 cm and the trees with diameter above 7 cm, while the species composition was derived from the growth area, BC2_SP_Hpa -BC2 index of similarity (Bray, Curtis 1957) between the trees with diameter below 7 cm and the trees with diameter above 7 cm calculated from the average tree heights of species, BP_ST_Ha -BP index of species evenness (Berger, Parker 1970) calculated from the sum of tree heights of trees with diameter above 7 cm, BUB_SP_Rr -BUB index of similarity (Baroni-Urbani, Buser 1976) between the trees with diameter below 7 cm and the trees with diameter above 7 cm, while the species composition was derived from the growth area, D_ST_Na -D index of species evenness (McIntosh 1967) of trees with diameter above 7 cm, DF1_SP_Hpa -DF1 index of similarity (Canberra distance) (Lance, Williams 1966) between the trees with diameter below 7 cm and the trees with diameter above 7 cm calculated from the average tree heights of species, E1_SP_Hr -E1 index of species evenness (Pielou 1975(Pielou , 1977 of all trees, while the species composition was derived from the sum of tree heights, E1_ST_Rr -E1 index of species evenness (Pielou 1975(Pielou , 1977 of trees with diameter above 7 cm, while the species composition was derived from the growth area, E3_SP_Hr -E3 index of species evenness (Heip 1974) of all trees, while the species composition was derived from the sum of tree heights, E3_ST_Hr -E3 index of species evenness (Heip 1974) of trees with diameter above 7 cm, while the species composition was derived from the sum of tree heights, E3_ST_Rr -E3 index of species evenness (Heip 1974) of trees with diameter above 7 cm, while the species composition was derived from the growth area, E5_SP_Hr -E5 index of species evenness (Hill 1973) of all trees, while the species composition was derived from the sum of tree heights, E5_ST_Hr -E5 index of species evenness (Hill 1973) of trees with diameter above 7 cm, while the species composition was derived from the sum of tree heights, ED1_SP_Ha …”

Section: Resultsmentioning

confidence: 99%

“…Biodiversity was quantified by the following basic indicators that describe species and structural diversity: (i) indices of species richness: N0 (Hill 1973), R1 (Margalef 1958), R2 (Menhinick 1964); (ii) indices of species evenness: BP (Berger, Parker 1970), E1 (Pielou 1975(Pielou , 1977, E3 (Heip 1974), E5 (Hill 1973), D (McIntosh 1967; (iii) indices of species heterogeneity: Si (Simpson 1949), H (Shannon, Weaver 1949), HB (Brillouin 1956); (iv) indices of similarity: QS (Sørensen 1948), BC (Bray, Curtis 1957), ED -Euclidian distance, BUB (Baroni-Urbani, Buser 1976), Y (Boyce 2003), DF -Canberra distance (Lance, Williams 1966), PS -proportional similarity (Czekanowski 1909); (v) other indicators: absolute and relative range of tree heights, species aggregation and mixture assessed in the field, volume of fine and coarse woody debris on a plot, number of vertical layers according to Zlatník (1976), number of shrub species, number of moss and lichen species.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Relation between selected indicators of forest stand diversity and quality of timber production in managed Central European forests

Merganič¹,

Merganičová²,

Marušák³

et al. 2016

J. For. Sci.

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ABSTRACT:The present study examines the relationship between the quality of timber production and the species and structural diversity of forest stands. The data used came from a regional forest inventory of the University Forest Enterprise "Kostelec nad Černými lesy", Czech Republic. The inventory was performed from 2009 to 2011 on 1,188 sample plots that represented 86 strata defined by the combination of three variables: site (5 categories), age (12 categories) and canopy cover (5 categories). On each sample plot, we quantified 171 partial biodiversity indicators that represented species or structural diversity. The quality of timber production was specified by four indicators quantified using local assortment tables. In total, we analysed 58,824 univariate linear regressions describing the relationships between diversity indicators and timber quality in individual strata. The results revealed that their relationship changes with stand age. The proportion of the best-quality assortments increases with the increasing species richness in all age categories.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Relation between selected indicators of forest stand diversity and quality of timber production in managed Central European forests

Merganič¹,

Merganičová²,

Marušák³

et al. 2016

J. For. Sci.

View full text Add to dashboard Cite

show abstract

“…We have Fourthly, we propose to adopt the abstract value of the Pearson's correlation coefficient value as the similarity measure. We have Fifthly, we propose to adopt Canberra distance [16][17][18], which is a metric function often used for data scattered around an origin. The Canberra distance is similar to the Manhattan distance and the distinction is that the absolute difference between the variables of the two objects is divided by the sum of the absolute variable values prior to summing.…”

Section: Guilt-by-association Modelmentioning

confidence: 99%

Prediction of Disease-associated Single Amino Acid Polymorphisms Based on Physiochemical Features

Wu¹,

Gan²,

Zhang³

et al. 2011

IJBBB

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Abstract-Benefiting from recent advancements of the next generation sequencing technology, it becomes more and more feasible to directly sequence candidate genetic regions and even the whole genome to get the information about rare genetic variants. Although several statistical methods have been developed to identify potential associations between multiple rare variants and a given disease of interest, these methods are quite sensitive to the inclusion of non-functional variants in their statistical analysis. In order to enhance the performance of these statistical methods for uncovering disease-associated rare variants, it is suggested that bioinformatics tools or filters should be adopted to make functional predictions of the variants before statistical analysis. In this paper, we propose to prioritize candidate genetic variants according to the guilt-by-association principle, which depends on the assumption that genetic variants associated with the same disease share some common physiochemical properties. Focusing on a specific type of genetic variants called single amino acid polymorphisms (SAAPs), we take advantages of 8 similarity measures based on physiochemical features of amino acids, sequence information of proteins, and multiple sequence alignment of protein families to illustrate the power of prioritizing candidate SAAPs for specific diseases. Systematic validation experiments demonstrate that our proposed approach is competent for effectively detecting associations between SAAPs and query diseases, while using the Canberra distance to measure the similarity between SAAPs can achieve the highest performance among all the methods compared.

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“…Although it has been stated that "the coefficient is not defined for continuously-varying numerical data" (Lance and Williams 1966), this is not relevant for polyclaves because the identification database will treat numeric ranges in most respects like other discrete categories (see below).…”

mentioning

confidence: 99%

“…The entropy H of a character takes a maximum value when the probabilities are uniformly distributed (Cover and Thomas 2006, Theorem 2.6.4), and that maximum is log n where n denotes "the number of elements in the range". This result has been stated as "maximal H… depends only on the number of states" (Abbott et al 1985, p.101), as "the base of the logarithms is an arbitrary choice" (Lance and Williams 1966), and as "if one wanted to confine H always to the range 0 to 1, then logarithms to base m can be used for characters with m states ... this is in effect just the same as multiplying H by a normalizing constant." (Pankhurst 1991, p. 192).…”

mentioning

confidence: 99%

Character Selection During Interactive Taxonomic Identification: “Best Characters”

Talent

Dickinson²,

Dickinson

2014

Biodiv. Inf.

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Abstract-Software interfaces for interactive multiple-entry taxonomic identification (polyclaves) sometimes provide a "best character" or "separation" coefficient, to guide the user to choose a character that could most effectively reduce the number of identification steps required. The coefficient could be particularly helpful when difficult or expensive tasks are needed for forensic identification, and in very large databases, uses that appear likely to increase in importance. Several current systems also provide tools to develop taxonomies or single-entry identification keys, with a variety of coefficients that are appropriate to that purpose. For the identification task, however, information theory neatly applies, and provides the most appropriate coefficient. To our knowledge, Delta-Intkey is the only currently available system that uses a coefficient related to information theory, and it is currently being reimplemented, which may allow for improvement. We describe two improvements to the algorithm used by Delta-Intkey. The first improves transparency as the number of remaining taxa decreases, by normalizing the range of the coefficient to [0,1]. The second concerns numeric ranges, which require consistent treatment of sub-intervals and their endpoints. A stand-alone Bestchar program for categorical data is provided, in the Python, R, and Java languages. The source code is freely available and dedicated to the Public Domain.

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Computer Programs for Hierarchical Polythetic Classification ("Similarity Analyses")

Cited by 332 publications

References 11 publications

Relation between selected indicators of forest stand diversity and quality of timber production in managed Central European forests

Relation between selected indicators of forest stand diversity and quality of timber production in managed Central European forests

Prediction of Disease-associated Single Amino Acid Polymorphisms Based on Physiochemical Features

Character Selection During Interactive Taxonomic Identification: “Best Characters”

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