KMeans - different output on the same data

Hi,

Please find the below code:

<operator name="Root" class="Process" expanded="yes">
    <operator name="ExampleSetGenerator" class="ExampleSetGenerator">
        <parameter key="target_function"	value="sum classification"/>
        <parameter key="number_examples"	value="50"/>
    </operator>
    <operator name="IOMultiplier" class="IOMultiplier">
        <parameter key="io_object"	value="ExampleSet"/>
    </operator>
    <operator name="KMeans" class="KMeans">
    </operator>
    <operator name="IOSelector" class="IOSelector">
        <parameter key="io_object"	value="ExampleSet"/>
        <parameter key="select_which"	value="2"/>
    </operator>
    <operator name="KMeans (2)" class="KMeans">
    </operator>
</operator>

I have generated a dataset(required for KMeans) and made a copy of the same, so now I have 2 copies of the same dataset. I then apply the "KMeans" operator on both the examplesets. But the cluster centroids and also the cluster groupings are different for two examplesets. Why is this? Is it dependent on some seed value?

If I re-run the code, the same examplesets are generated with the same cluster centroids and groupings for the first and second examplesets. Why is is this? I then clicked the option, "use_local_random_seed" in KMeans. This made the cluster centroids and grouping look identical for both the data.

Questions:
1. What actually happens by the usage of "use_local_random_seed"?

2. The cluster centroids and groupings of the first and second examplesets are always the same irrespective of how many times we run it. But the KMeans applied on the same data in a single run is always different. Does this mean, RM when detects a KMeans operator for the first time applies a seed "A" and for the second time "B" always?

3. How do we choose the "use_local_random_seed"? What are its minimum and maximum values?

4. For simplicity, one can consider the below code too.

<operator name="Root" class="Process" expanded="yes">
    <operator name="ExampleSetGenerator" class="ExampleSetGenerator">
        <parameter key="target_function"	value="sum classification"/>
        <parameter key="number_examples"	value="50"/>
    </operator>
    <operator name="KMeans" class="KMeans" breakpoints="before">
    </operator>
    <operator name="KMeans (2)" class="KMeans" breakpoints="before">
    </operator>
</operator>

Many Thanks,
Shubha.