 what are different functions which we can perform with VECA one important function is called pre-processing pre-processing means to prepare data for further handling or classification clustering for our data now VECA is a very strong tool for pre-processing type things now what we have is again I will start we have 6 tabs the first tab is for pre-process now we saw that when we clicked on the sample length or the pattern length so we have some properties of it and its visualization came I think this is clear to everyone now moving on now what we want now we see that some pre-processing filters will come in this tab what was the filter that when we put this function then the values of our pattern length will change now it will change let's see like when I clicked on the filter then this tab opened VECA major tag then filters then unsupervised attribute and instances this tab is open to us I clicked further but before that let's say I opened this file and it didn't come to me now what happened when I clicked attribute meaning that the tab I had VECA, filters, unsupervised attribute and what I typed I discretized it this means that the attributes of the pattern length we want to discretize them we want to convert them in a discrete form so I clicked on this tab now what will I have what effect will come something like this that it will ask us that this discretized tag set the parameters of it these are parameters of discretized tab tab is very simple as we talked about choose the name of the tab now what happened those are the tab attributes, instances first class, bin 10 fine bin numbers, false all this information let's run it now what we did use equal frequency we also false it we set these values whatever values you want to set we set these values and in the end we clicked OK button now we applied it so this is our information now you can see what happened that the pattern length we converted it in a discrete form that whatever labels we have 23, 14, 11, and so on so what is happening that whatever length we have length is 1.45 to 1.55 those are 14 instances i.e. whose count is not there and minus 1.45 are its 23 instances 1.55 to 1.8 are 11 instances 1.8 to 3.95 to 14 and so on so what happened basically we made bins data let's say if we had the first data whatever we saw earlier let's say we had start from 0.3 to 5.05 now what we did we put it in different bins and we discretized it in a discrete form this is our pre-processing step instead of putting its data in a zigzag form in a histogram form we showed it in discrete intervals or in a bins form so this is a pre-processing i have just put a filter although you can see that in it there are many filters which are available in GUI now what is your duty that you take these filters open this file open the same file apply different filters apply different filters and then these properties that is its attribute and then these graphs compare them and then see for your problem which filter is best suited which can be useful to explain your problem in a better way