Dissimilarity Computation
Provides the generic function dissimilarity
and the S4 methods to
compute and returns distances for binary data in a matrix
,
transactions
or associations
which
can be used for grouping and clustering. See Hahsler (2016)
for an introduction to distance-based
clustering of association rules.
dissimilarity(x, y = NULL, method = NULL, args = NULL, ...) ## S4 method for signature 'itemMatrix' dissimilarity(x, y = NULL, method = NULL, args = NULL, which = "transactions") ## S4 method for signature 'associations' dissimilarity(x, y = NULL, method = NULL, args = NULL, which = "associations") ## S4 method for signature 'matrix' dissimilarity(x, y = NULL, method = NULL, args = NULL)
x |
the set of elements (e.g., |
y |
|
method |
the distance measure to be used. Implemented measures
are (defaults to
For associations the following additional measures are available:
|
args |
a list of additional arguments for the methods. |
which |
a character string indicating if the dissimilarity should be
calculated between transactions/associations (default)
or items (use |
... |
further arguments. |
returns an object of class dist
.
Michael Hahsler
Aggarwal, C.C., Cecilia Procopiuc, and Philip S. Yu. (2002) Finding localized associations in market basket data. IEEE Trans. on Knowledge and Data Engineering 14(1):51–62.
Dice, L. R. (1945) Measures of the amount of ecologic association between species. Ecology 26, pages 297–302.
Gupta, G., Strehl, A., and Ghosh, J. (1999) Distance based clustering of association rules. In Intelligent Engineering Systems Through Artificial Neural Networks (Proceedings of ANNIE 1999), pages 759-764. ASME Press.
Hahsler, M. (2016) Grouping association rules using lift. In C. Iyigun, R. Moghaddess, and A. Oztekin, editors, 11th INFORMS Workshop on Data Mining and Decision Analytics (DM-DA 2016).
Sneath, P. H. A. (1957) Some thoughts on bacterial classification. Journal of General Microbiology 17, pages 184–200.
Sokal, R. R. and Michener, C. D. (1958) A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin 38, pages 1409–1438.
Toivonen, H., Klemettinen, M., Ronkainen, P., Hatonen, K. and Mannila H. (1995) Pruning and grouping discovered association rules. In Proceedings of KDD'95.
## cluster items in Groceries with support > 5% data("Groceries") s <- Groceries[,itemFrequency(Groceries)>0.05] d_jaccard <- dissimilarity(s, which = "items") plot(hclust(d_jaccard, method = "ward.D2"), main = "Dendrogram for items") ## cluster transactions for a sample of Adult data("Adult") s <- sample(Adult, 500) ## calculate Jaccard distances and do hclust d_jaccard <- dissimilarity(s) hc <- hclust(d_jaccard, method = "ward.D2") plot(hc, labels = FALSE, main = "Dendrogram for Transactions (Jaccard)") ## get 20 clusters and look at the difference of the item frequencies (bars) ## for the top 20 items) in cluster 1 compared to the data (line) assign <- cutree(hc, 20) itemFrequencyPlot(s[assign==1], population=s, topN=20) ## calculate affinity-based distances between transactions and do hclust d_affinity <- dissimilarity(s, method = "affinity") hc <- hclust(d_affinity, method = "ward.D2") plot(hc, labels = FALSE, main = "Dendrogram for Transactions (Affinity)") ## cluster association rules rules <- apriori(Adult, parameter=list(support=0.3)) rules <- subset(rules, subset = lift > 2) ## use affinity to cluster rules ## Note: we need to supply the transactions (or affinities) from the ## dataset (sample). d_affinity <- dissimilarity(rules, method = "affinity", args = list(transactions = s)) hc <- hclust(d_affinity, method = "ward.D2") plot(hc, main = "Dendrogram for Rules (Affinity)") ## create 4 groups and inspect the rules in the first group. assign <- cutree(hc, k = 3) inspect(rules[assign == 1])
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