R中字符串的聚类序列

Question

我需要以下数据：

attributes <- c("apple-water-orange", "apple-water", "apple-orange", "coffee", "coffee-croissant", "green-red-yellow", "green-red-blue", "green-red","black-white","black-white-purple")
attributes 

           attributes 
1  apple-water-orange
2         apple-water
3        apple-orange
4              coffee
5    coffee-croissant
6    green-red-yellow
7      green-red-blue
8           green-red
9         black-white
10 black-white-purple

我想要的是另一列，它根据观察相似性为每一行分配一个类别。

category <- c(1,1,1,2,2,3,3,3,4,4)
df <- as.data.frame(cbind(df, category))

       attributes     category
1  apple-water-orange        1
2         apple-water        1
3        apple-orange        1
4              coffee        2
5    coffee-croissant        2
6    green-red-yellow        3
7      green-red-blue        3
8           green-red        3
9         black-white        4
10 black-white-purple        4

它是广义上的聚类，但我认为大多数聚类方法仅针对数字数据，one-hot-encoding有很多缺点（这是我在网上看到的）。

有人知道如何完成这项任务吗？也许一些单词匹配方法？

如果我能根据参数调整相似度（粗略与体面"clustering"）也很好。

提前感谢任何想法！

Answer 1

所以我想到了两种可能性。选项 1：使用“one-hot-encoding”，只要 apple/apples 与 apple/orange 同样不同，它就简单明了。我使用 Jaccard 索引作为距离度量，因为它在处理重叠集时效果相当好。选项 2：使用局部序列比对算法，并且应该对 apple/apples 与 apple/orange 之类的事情非常稳健，它还会有更多的调整参数，这可能需要时间来优化您的问题。

library(reshape2)
library(proxy)

attributes <- c("apple-water-orange", "apple-water", "apple-orange", "coffee", 
                "coffee-croissant", "green-red-yellow", "green-red-blue", 
                "green-red","black-white","black-white-purple")
dat <- data.frame(attr=attributes, row.names = paste("id", seq_along(attributes), sep=""))
attributesList <- strsplit(attributes, "-")

df <- data.frame(id=paste("id", rep(seq_along(attributesList), sapply(attributesList, length)), sep=""), 
                 word=unlist(attributesList))

df.wide <- dcast(data=df, word ~ id, length)
rownames(df.wide) <- df.wide[, 1] 
df.wide <- as.matrix(df.wide[, -1])

df.dist <- dist(t(df.wide), method="jaccard")
plot(hclust(df.dist))
abline(h=c(0.6, 0.8))
heatmap.2(df.wide, trace="none", col=rev(heat.colors(15)))

res <- merge(dat, data.frame(cat1=cutree(hclust(df.dist), h=0.8)), by="row.names")
res <- merge(res, data.frame(cat2=cutree(hclust(df.dist), h=0.6)), by.y="row.names", by.x="Row.names")
res

您会发现可以通过调整切割树状图的位置来控制分类的粒度。

这里是使用“Smith-Waterman”对齐方式（本地）对齐方式

Biostrings 是 Bioconductor project. The SW algorithm finds the optimal local (non-end-to-end) alignment of two sequences (strings). In this case you can again use cutree to set your categories but you can also tune the scoring function 的一部分，可以满足您的需要。

library(Biostrings)
strList <- lapply(attributes, BString)

swDist <- matrix(apply(expand.grid(seq_along(strList), seq_along(strList)), 1, function(x) {
  pairwiseAlignment(strList[[x[1]]], strList[[x[2]]], type="local")@score
}), nrow = 10)

heatmap.2(swDist, trace="none", col = rev(heat.colors(15)),
          labRow = paste("id", 1:10, sep=""), labCol = paste("id", 1:10, sep=""))

R中字符串的聚类序列

Cluster sequences of strings in R

r

cluster-analysis

textmatching

string-matching

categorical-data

这里是使用“Smith-Waterman”对齐方式（本地）对齐方式