7 highly effective Data Mining techniques.

By arvind |Email | Oct 16, 2018 | 4185 Views

So what is data mining you ask? An easy well to tell is - it is a process that creates new information from the big bulks of information that is already present there. And well if you are thinking "data mining" refers to the eradication of new data, but wait that is not what it is well not in this case, data mining is about deducing patterns and fresh knowledge from the data that you already have in your data base or files or wherever you keep your data. 
Data mining specialists have careers dedicated for better understanding about how to process and draw conclusions from the large amounts of information. They rely on technologies and techniques from the crossroads of database management.
For this there are some techniques that these data mining specialists use so all this data mining could happen. This article is about highly effective data mining techniques.
1. Tracking patterns. A basic technique in data mining is learning how to recognize patterns in your data sets. This recognition is usually of some aberration in your data happening at regular breaks, or an ebb and flow of a certain variable over time. For example, you might see that your sales of a certain product seem to spike just before the holidays, or notice that warmer weather drives more people to your website.
2. Classification. Classification is a more complex data mining technique that pushes you to collect various attributes together into distinct categories, which can then be used by use to draw further conclusions, or serve some function. For example, if you're evaluating data on individual customers' financial backgrounds and purchase histories, you might be able to classify them as "low," "medium," or "high" credit risks. You could then use these classifications to learn even more about those customers.
3. Association. Association is related to tracking patterns, but is more specific to dependently linked variables. In this case, you'll look for specific events or attributes that are highly correlated with another event or attribute; for example, you might notice that when your customers buy a specific item, they also often buy a second, related item. This is usually what's used to populate "people also bought" sections of online stores.
4. Outlier detection. In many cases, simply recognizing the overarching pattern can't give you a clear understanding of your data set. You also need to be able to identify anomalies, or outliers in your data. For example, if your purchasers are almost exclusively male, but during one strange week in July, there's a huge spike in female purchasers, you'll want to investigate the spike and see what drove it, so you can either replicate it or better understand your audience in the process.
5. Clustering. Clustering is very similar to classification, but involves grouping chunks of data together based on their similarities. For example, you might choose to cluster different demographics of your audience into different packets based on how much disposable income they have, or how often they tend to shop at your store.
6. Regression. Regression, used primarily as a form of planning and modelling, is used to identify the likelihood of a certain variable, given the presence of other variables. For example, you could use it to project a certain price, based on other factors like availability, consumer demand, and competition. More specifically, regression's main focus is to help you uncover the exact relationship between two (or more) variables in a given data set.
7. Prediction. Prediction is one of the most valuable data mining techniques, since it's used to project the types of data you'll see in the future. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. For example, you might review consumers' credit histories and past purchases to predict whether they'll be a credit risk in the future.


Source: HOB