how to do k means clustering on sift features in matlab?

I want to perform k-means clustering on sift features which is in the form of a 509X508 matrix.I got a code for k-means clustering from mathworks. But i don't know how to give input for the algorithm. Pls help...

Solution

From here.

IDX = kmeans(X,k) partitions the points in the n-by-p data matrix X into k clusters. . Rows of X correspond to points, columns correspond to variables. kmeans returns an n-by-1 vector IDX containing the cluster indices of each point.

How can I close over variables in kdb/Q?
When is the EACH operator extension necessary in K besides mod/rotate?
Handling single-character strings - in a function or in its caller? ssr()
Kdb+ data fomat when writing to a file
How to convert a symbol to a string in kdb+?
Sum of each two elements using vector functions
A dictionary with a single value and multiple keys
Table transformation, table as list of dicts
Accumulator gives different result then direct function applying
Reshape [cols;table]
FK field over IPC
Protected execution, 2 cases
Enums for tables
Converge (fixed point) syntax difference in q and k
.Q.trp and bt handling
NULLs in q and in k.h
Strange view declaration behaviour
How to build a parse-tree of projections?
Could not evaluate manually created equial ~ parse tree
Select distinct for all columns from keyed table
Parallel execution: blocking receive, deferred synchronous
Multiple variable assignment in q
Select a table from the inside of external select
Select when one of filter-column may not exists
What is the meaning of `s attribute on a table?
On parallel execution - which side reports about an error?
Validate if a keyed table have unique keys
Applying dictionary to dictionary
About xkey implementation
Parse tree built on values from vars