Basic Data Types | Introduction to Data Mining | Part 3

Key Takeaways

all right we can move on to data set classification so data sets are there are a lot of different types of data sets and they require different approaches to analysis the pre-processing steps the modeling steps pretty much everything that you do with these different types of data sets is going to be different the kinds of models you use the kinds of visualizations you construct the kind of cleaning that is proper for that kind of data um understanding the structure of your data at the beginning is very important to not wasting time and not producing incorrect results uh and it's in this step the understanding the type this the the structure of your data that things like domain knowledge tend to be very important um but there are still certainly categories that tend to be similar no matter what domain they're in so uh we'll talk talk about these three different kinds of types of data sets records graphs and ordered data sets uh in a little bit more detail coming up here so record data is data that consists of a collection of Records Each of which consists of a fixed set of attributes so this uh tax ID so this particular data set which uh I use in some in some in several places um is a record data we have every data object has one tax ID has a value of whether they asked for refund marital status uh whether they're single married or divorced a taxable income field and a and whether they cheated on their taxes or not so that's what sort of the structure of this data set so any data which consists of this kind of collection of Records which consist of a fix set of attributes you almost always represent this kind of data as a table um whether a database table or or a spreadsheet or something like that and it's the most common kind of data uh so a lot of people will if you talk about data or data sets this is what they visualize entirely is record data um so it's sort of your your your most common and sort of fundamental kind of data set So within record data there are a few useful subsets so this record data with the tax data has some categorical values and then one ordinal variable uh so tax ID is ordinal right or is it it's really more of a of a nominal variable when you think about it because ordering doesn't necessarily matter right sure it takes numbers but 10 is not meaningfully different from five there's no ordering implied here so tax ID is a nominal field a nominal categorical field uh tax refund is a categorical field marital status also taxable income is a continuous field so most data that you encounter has mixed data types like this you have some categorical some numeric uh and that's sort of your traditional type of record data if on the other hand your record data consists entirely of numeric attributes so this is entire L continuous uh entirely interval or ratio variables then we can think of it as a mathematical Matrix rather than just a table so we would have an M byn Matrix there are M rows one for each data object n columns one for each attribute and this is nice because we can think of these data objects as points in a multi-dimensional space where each attribute is represented along one dimension and that allows us to use a number of numeric techniques specifically involving distance that some algorithms not only make the make some algorithms easier but which some algorithms require there's a number of algorithms that require you to have data Matrix data all numeric data