Displaying 1 result from an estimated 1 matches for "case10".
Did you mean:
case1
2012 Dec 06
1
clustering of binary data
...case1 0 0 0
0 0 1 0 0 1 1 0 0 0 case2 0 0 0 0 0 1 0 NA NA 1 0 0 0 case3 0 0 0 0 0 1 0
0 1 1 0 0 0 case4 1 0 0 0 0 1 0 1 0 1 0 0 0 case5 0 0 0 0 0 1 0 0 1 1 0 0
0 case6 0 1 0 0 0 1 0 1 0 1 0 0 0 case7 0 1 0 0 0 1 0 0 1 1 0 0 0 case8 0
0 0 0 0 1 0 1 0 1 0 0 0 case9 0 0 0 0 0 1 0 1 0 1 0 0 0 case10 0 0 0 0 0 1
0 0 1 1 0 0 0 case11 1 0 0 1 0 1 1 1 0 1 0 0 0 case12 0 0 0 1 1 0 1 1 0 1
0 0 0 .....
So, my questions are the following: Is the Jaccard index a good strategy
for my kind of data? Is binary distance used in pvclust is theoretically
more correct? Is there any alternative...