Data Mining Techniques and Methods

The Techniques of Data Mining Today, there exist various techniques of data mining used in data mining projects. 

  Association 

In this technique, patterns are discovered basing on the link between things a similar transaction. This explains why the technique of association is often referred to as a relationship technique. This method is mostly applied in business analysis in order to establish the most frequently purchased set of products. Entrepreneurs are applying the technique of association in discovering the buying trends of customers basing on the history of sales data.

This may help them establish that clients always purchase a pack of chips when buying beers and business owners can, therefore, place chips and beers close to each other in order to save the customer’s time spent in the store, and in the long run, increasing their sales. 

 Classification 

This is a machine learning-based technique of data mining. Classification is applied in classifying every item in sets of data into a single large predefined set of collection. This technique puts into use physical ways including programming linear, the process of decision making, logical networks, and statistical methods.

Clustering 

Clustering is a technique in data mining that puts into use of a group of objects that contain the same characteristics while using the technique of automation. It defines the categories and categorizes elements into every class.

The challenge in this situation can be keeping these different types of books in a certain order such that users can pick various books on a specific topic with so much ease. This structure of mining is connected to a data source but does not have any data unless you process it.  When applying the technique of clustering, you can store books that talk about the same topic on a single shelf or cluster and identify it with a relatable nametag. So if users have to pick books on a particular topic, they will only have to walk to that location rather than doing rounds in the entire library.

Prediction 

Just as its term implies, prediction is a mining of data technique that establishes the link between variables that are independent and the link between dependent & independent techniques of variables.

example

the technique of prediction can be applied in the sales to foretell profits in the coming days if the sale can be considered to be an independent variable with gains being considered as a dependent variable. Basing on the previous profit data and sales, you can come up with a fitted representation graph that can be used in profit prediction. 

Sequential Patterns 

Mining of data technique that looks to establish resembling patterns, trends, or events in transactional data in a certain period of time. In business sales, establishing patterns can help businesses locate a list of commodities that consumers buy together at various times.

  Decision Trees

 one of the most applied techniques in data mining because it contains a model that is easily understandable to users. When using this technique, the foundation of the decision is a good question that cans several true feedbacks.  Every answer results to a set of requirements that help you establish the data model.

Read 👉Top 20 Python libraries for Data Science

What are the Methods of Data Mining? 

It is possible to build a predictive model from different sets of data. Each dataset is created using a unique technique. The following are some of the common methods used for data mining: 

Regression analysis

 This is the process of studying and evaluating the nature of relationships between different variables. The emphasis of such studies is usually to gauge the relationships while at the same time accounting for error reduction. 

Cluster analysis 

In this process, the analyst studies different groups of data and tries to understand them from their unique characteristics. Each cluster is built according to specific features. Members of a cluster, therefore, are expected to have similar behaviours. 

Data classification 

Data classification is about narrowing down data groups into unique categories. The categories must first be built according to specific instructions, then any data that meets the said instructions are moved into their respective classification. A good example of this is spam mail. 

 Analyzing outliers

 This is a process where you study the outliers to determine why they exist. Outliers usually appear in a dataset when some data goes against an established pattern. This is data that does not align along a determined plane as expected.

Correlation and association analysis

 You can also study the data to determine whether there is a relationship between different variables. In particular, you should be looking at data on variables whose relationship might not be explicit. A good example is the Walmart beer-diaper case study. They realized a correlation between beer and diaper sales on Friday evenings. These are two products that should ideally not share any relationship. However, upon prodding further, it emerged that the purchases were related because most of the men who purchased diapers were young or new fathers. While picking up the diapers, they figured they might as well grab a few beers to enjoy at home.