9 Data Mining Functionalities

Data mining is an automatic or semi-automatic process often used to extract information from large quantities of data. Such knowledgeable information might be useful in forming a summary of the input data. It is a technical methodology that identifies patterns, rules, or trends which contextually explain data behaviour. 

Data mining functionalities use mathematical, statistical, machine learning, AI, and other processes to find patterns and trends that were previously impossible to find using traditional data exploration techniques. It is a practical and incredibly convenient method for handling enormous amounts of data. 

In this article, you will explore the different types of data mining functionalities and their processes to help you add new skills to your toolbox. 

Types of data mining tasks

The two categories of data mining are: 

• Descriptive data mining: It highlights common characteristics without any historical or previous data input. Examples are count and average. 

• Predictive data mining: It can predict important business metrics using previously available information based on the data’s linearity. For example, data mining might predict the business of one quarter based on the information available about the previous quarters. 

Different types of data mining functionalities

Data mining functionalities represent the patterns that need to be found in data mining activities. Let's explore nine data mining functionalities.

1. Classification

Classification is the separation of data elements in a collection or classes according to their predetermined functionalities and characteristics. This process can help organise and classify new information by its unknown classification. 

In data mining, the classifier refers to the classification technique and the observations you make using the classifier, which are instances. Classification algorithms can be used when working with qualitative variables. 

Such a classification process may employ decision trees, logic regression, Naive Bayes, and random forest techniques. Retrieving these approaches can identify future data.

An example of using classification is marketers who want to segment their audience. To create more precise and successful marketing campaigns, they divide their target clients into several classifications using this data mining technique.

2. Association analysis

Association analysis helps find relationships between items that commonly appear together. It is also called market basket analysis and is one of the more popular data mining functionalities in sales. It consists of rules and factors that specify data grouping within situations. 

Both frequent item sets and association rules can describe these unique relationships:

• Frequent items sets refer to a group of objects or elements commonly appearing together, such as items in a grocery list (e.g., rice, turmeric powder, onions, and soy milk)

• Association rules suggest a significant relationship between two items, such as people who buy bread are likely also to buy butter. 

An antecedent (if) indicates the likelihood of finding a consequent (then) in data collection. Association analysis depends on this two-part rule, suggesting their connection. 

This technique helps predict consumer behaviour in sales. For example, if the audience at a movie theatre purchases a bucket of popcorn, there is a high chance that they will also buy a cold drink. 

3. Cluster analysis

In cluster analysis, similar data are grouped—or clustered—together under an unknown class label. Data is split into groups by clustering algorithms based on similarities, and the data groups are more similar than the other data groups. 

Deep learning, image processing, pattern identification, and natural language processing use cluster analysis. 

Cluster analysis is similar to classification, but the main distinction is that the elements are predefined in classification. Clustering analysis, on the other hand, achieves the same outcome as a classification without having specified classes; thus, clustering is also described as an unsupervised classification or an unsupervised learning algorithm. 

4. Data characterisation

Data characterisation summarises general features or elements of the data to establish specific rules for defining a target class. An attribute-oriented induction technique allows for characterising the data without much user involvement. The "characteristics rule of the target class" helps present the relationship between the data characterised and visualised in various forms, such as graphs, pie charts, bar diagrams, or table formats. 

5. Prediction

Prediction is one of the data mining functionalities you can use to identify missing or ambiguous data set elements. Businesses can predict the outcomes of any given occurrence, whether favourable or unfavourable, using linear regression models based on historical data to produce numerical forecasts. You can carry out predictions in the following two ways: 

• Data prediction: Predict any unknown or missing data from a data set via prediction analysis.

• Class predictions: Using a previously constructed class model to predict the class label.

6. Data discrimination

Data discrimination occurs when you treat a data collection or source separately from the others, whether on purpose or accidentally. Thus, this data mining functionality is similar to data characterisation and aids in separating unusual data sets. Typically, it helps compare data from two classes and maps the target class with a predefined class. 

7. Evolution analysis

Evolution analysis is the study of data sets that may have undergone a stage of transformation or change. It provides time-related data clustering and assists in finding trends or changes with features like periodicity, time-series data, and trend similarity.

In addition to aiding in data classification, characterisation, discrimination, and grouping for multivariate time series, the evolution analysis model represents evolving trends in data. 

8. Outlier analysis

Analysing outliers helps understand data quality. An outlier is a data anomaly. A greater number of outliers in a data set implies a lower quality of the data. Therefore, using a data set with high outliers is not a wise option for finding patterns in the data or drawing any conclusions. 

This analysis technique is helpful when the data algorithm fails to classify data and you encounter data with different attributes that don’t match any other class or general model. 

However, it is still crucial to maintain track of odd data so that this data mining technique can identify any abnormalities early and you can foresee any potential effects on the business. 

9. Correlation analysis

You can also use a mathematical method known as correlation to assess whether or how strongly two components relate. Correlation establishes the degree to which two continuously monitored numerical variables (e.g., height and weight) relate. Researchers can use this analysis to determine whether any probable connections exist between the variables they are studying, such as the tendency for taller persons to be heavier. 

Learn data mining with Coursera

Data mining functionalities help predict and classify data according to its elements and features. This automatic process is beneficial when dealing with large data sets and aids businesses in adapting quickly to fluctuating trends and events that might hamper business. Learn more about data mining to understand data mining functionalities for both structured and unstructured data.