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Cluster Analysis

Cluster Analysis. Market Segmentation Document Similarity. Segment Members. Segment Members. = 64. Biz. Math. Tech. Main Groups. Hierarchical Clustering.

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Cluster Analysis

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  1. Cluster Analysis • Market Segmentation • Document Similarity

  2. Segment Members

  3. Segment Members = 64 Biz Math Tech Main Groups

  4. Hierarchical Clustering • Each object is assigned to its own cluster and then the algorithm proceeds iteratively, at each stage joining the two most similar clusters, continuing until there is just a single cluster. • At each stage distances between clusters are recomputed by the Lance–Williams dissimilarity update formula according to the particular clustering method being used.

  5. Hierarchical Clustering biztech <- read.csv("survey-biztech.csv") biztech <- as.matrix(biztech) #hierarchical clustering d <- dist(as.matrix(biztech)) dm <- data.matrix(d) write.csv(dm, "distance_matrix.csv")

  6. hc <- hclust(d) plot(hc) rect.hclust(hc, k=6, border="red")

  7. Hierarchical Clustering ct <- cutree(hc, k=6) #write to file write.csv(ct, "survey-hclust.csv")

  8. hierarchical clustering is very expensive in terms of time complexity • though it provides better result

  9. Cold Weather

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