CODEWITHSUNDEEP
rcluster-analysis
Nitin Wader
Nitin Wader

Reputation: 13

Clustering based on connectivity of points

I have 1 million records of lat long [5 digits precision] and Route. I want to cluster those data points.

I dont want to use standard k-means clustering as I am not sure how many clsuters [tried Elbow method but not convinced].

Here is my Logic -

1) I want to reduce width of lat long from 5 digits to 3 digits.

2) Now lat longs which are in range of +/- 0.001 are to be clustered in once cluster. Calculate centroid of cluster.

But in doing so I am unable to find good algorithm and R Script to execute my thought code.

Can any one please help me in above problem.

Thanks,

Upvotes: 0

Views: 528

Answers (1)

rahnema1
rahnema1

Reputation: 15867

Clustering can be done based on connected components.

All points that are in +/-0.001 distance to each other can be connected so we will have a graph that contains subgraphs that each may be a single poin or a series of connected points(connected components) then connected components can be found and their centeroid can be calculated. Two packages required for this task :

1.deldir to form triangulation of points and specify which points are adaject to each other and to calculate distances between them.

2 igraph to find connected components.

library(deldir)
library(igraph)
coords <- data.frame(lat = runif(1000000),long=runif(1000000))

#round to 3 digits
coords.r <- round(coords,3)

#remove duplicates
coords.u <- unique(coords.r)

# create triangulation of points. depends on the data may take a while an consume more memory
triangulation <- deldir(coords.u$long,coords.u$lat)

#compute distance between adjacent points
distances <- abs(triangulation$delsgs$x1 - triangulation$delsgs$x2) +
            abs(triangulation$delsgs$y1 - triangulation$delsgs$y2)

#remove edges that are greater than .001
edge.list <- as.matrix(triangulation$delsgs[distances < .0011,5:6])
if (length(edge.list) == 0) { #there is no edge that its lenght is less than .0011
    coords.clustered <- coords.u
} else { # find connected components

    #reformat list of edges so that if the list is 
    #   9 5
    #   5 7
    #so reformatted to
    #   3 1
    #   1 2
    sorted <- sort(c(edge.list), index.return = TRUE)
    run.length <- rle(sorted$x)
    indices <- rep(1:length(run.length$lengths),times=run.length$lengths)
    edge.list.reformatted <- edge.list
    edge.list.reformatted[sorted$ix] <- indices

    #create graph from list of edges
    graph.struct <- graph_from_edgelist(edge.list.reformatted, directed = FALSE)

    # cluster based on connected components
    clust <- components(graph.struct)

    #computation of centroids
    coords.connected <- coords.u[run.length$values, ]
    centroids <- data.frame(lat = tapply(coords.connected$lat,factor(clust$membership),mean) ,
                           long = tapply(coords.connected$long,factor(clust$membership),mean))

    #combine clustered points with unclustered points
    coords.clustered <- rbind(coords.u[-run.length$values,], centroids)

    # round the data and remove possible duplicates
    coords.clustered <- round(coords.clustered, 3)
    coords.clustered <- unique(coords.clustered)
}

Upvotes: 1

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