gstsm {gstsm}

GSTSM

Description

S3 class definition for GSTSM.

Usage

gstsm(sts_dataset, spatial_positions, gamma, beta, sigma)

Arguments

sts_dataset

STS dataset

spatial_positions

set of spatial positions

gamma

minimum temporal frequency

beta

minimum group size

sigma

maximum distance between group points

Details

This algorithm is designed to the identification of frequent sequences in STS datasets from the concept of Solid Ranged Groups (SRG). GSTSM is based on the candidate-generating principle. The goal is to start finding SRGs for sequences of size one. Then it explores the support and the number of occurrences of SRGs for larger sequences with a limited number of scans over the database.

Value

a GSTSM object

Examples

library("gstsm")

D <- as.data.frame(matrix(c("B", "B", "A", "C", "A",
              "C", "B", "C", "A", "B",
              "C", "C", "A", "C", "A",
              "B", "B", "D", "A", "B",
              "B", "D", "D", "B", "D"
            ), nrow = 5, ncol = 5, byrow = TRUE))

ponto <- c("p1", "p2", "p3", "p4", "p5")
x <- c(1, 2, 3, 4, 5)
y <- c(0, 0, 0, 0, 0)
z <- y
P <- data.frame(ponto=ponto, x=x, y=y, z=z, stringsAsFactors = FALSE)

gamma <- 0.8
beta <- 2
sigma <- 1

gstsm_object <- gstsm(D, P, gamma, beta, sigma)

result <- mine(gstsm_object)