interval_similarity: Similarity Measures for Interval Data

In dataSDA: Datasets and Basic Statistics for Symbolic Data Analysis

Similarity Measures for Interval Data

Description

Functions to compute similarity measures between interval-valued observations.

Usage

int_jaccard(x, var_name1, var_name2, ...)

int_dice(x, var_name1, var_name2, ...)

int_cosine(x, var_name1, var_name2, ...)

int_overlap_coefficient(x, var_name1, var_name2, ...)

int_tanimoto(x, var_name1, var_name2, ...)

int_similarity_matrix(x, method = "jaccard", ...)

Arguments

x	interval-valued data with symbolic_tbl class.
var_name1	the first variable name or column location.
var_name2	the second variable name or column location.
...	additional parameters
method	similarity method for int_similarity_matrix: "jaccard", "dice", or "overlap".

Details

These functions compute various similarity measures:

• int_jaccard: Jaccard similarity coefficient

• int_dice: Dice similarity coefficient

• int_cosine: Cosine similarity

• int_overlap_coefficient: Overlap coefficient

• int_tanimoto: Tanimoto coefficient (generalized Jaccard)

• int_similarity_matrix: Pairwise similarity matrix across all observations

All similarity measures range from 0 (no similarity) to 1 (perfect similarity).

Value

A numeric matrix or value

Author(s)

Han-Ming Wu

See Also

int_dist int_cor int_jaccard

Examples

data(mushroom.int)

# Jaccard similarity
int_jaccard(mushroom.int, "Pileus.Cap.Width", "Stipe.Length")

# Dice coefficient
int_dice(mushroom.int, 2, 3)

# Cosine similarity
int_cosine(mushroom.int, 
           var_name1 = c("Pileus.Cap.Width"), 
           var_name2 = c("Stipe.Length", "Stipe.Thickness"))

# Overlap coefficient
int_overlap_coefficient(mushroom.int, 2, 3:4)

# Tanimoto coefficient
int_tanimoto(mushroom.int, "Pileus.Cap.Width", "Stipe.Length")

# Similarity matrix across all observations
int_similarity_matrix(mushroom.int, method = "jaccard")

dataSDA documentation built on June 12, 2026, 9:06 a.m.