1. Cluster Analysis Market Segmentation Document Similarity

2. Segment Members

3. Biz Tech Math = 64 Main Groups

4. 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. Hierarchical Clustering

6. 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") Hierarchical Clustering

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

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

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

12. Cold Weather