Data Mining Functionalities – An Overview

Data mining is a technical methodology to detect information from huge data sets. The main objective of data mining is to identify patterns, trends, or rules that explain data behaviour contextually. The data mining method uses mathematical analysis to deduce patterns and trends, which were not possible through the old methods of data exploration. Data mining is a handy and highly convenient methodology for dealing with vast volumes of data. In this article, we explore some data mining functionalities that are measured to predict the type of patterns in data sets.  

To learn more about data mining, read – What is Data Mining

Data Mining Functionalities

We have listed some most popular functionalities of data mining, such as –

• Classification
• Association Analysis
• Cluster Analysis
• Data Characterization
• Data Discrimination
• Prediction
• Outlier Analysis
• Evolution Analysis

Classification

As the name suggests, classification is the technique of categorizing elements in a collection, basis their predefined functionalities and properties. In classification, the model can classify new instances whose classification is unknown. These particular instances used to create the model are called training data. Such a classification mechanism uses if-then, decision trees, neural networks, or even a set of classification rules. These methods can be retrieved to identify future data. It is used to build predictive models that can assign new data points to the appropriate class or category.

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Association Analysis

Association Analysis is also called Market Basket Analysis. It is a prevalent data mining methodology with usage in sales. Association analysis helps to find relations between elements frequently occurring together. It is made up of a series of sets of elements and rules that describe how these are grouped within the cases. Association rules are used to predict the presence of an element in the database and are based on the manifestation of a specific element identified as important. Association analysis is based on 2 parts rule –

antecedent (if)

consequent(then) –

An antecedent (if) points towards a degree of discovering a consequent (then) in the data set. It suggests that they are associated.

One example to understand this better can be – If a person buys popcorn in the theatre, there is a 60% chance that he will buy a cold drink. This way, a prediction can be made on the consumer’s shopping behaviour.

Cluster Analysis

The cluster analysis process is similar to that of classification. In cluster analysis, similar data types are grouped; the only difference is that the class label is unknown. Clustering algorithms divide the database similarities, and the grouped data are more similar to each other than the data in other groups. Cluster analysis is used in machine learning, deep learning, image processing, pattern recognition, NLP, etc.

Data Characterization

Data characterization involves summarizing the generic data features, which can result in specific rules to define a target class. An attribute-oriented induction technique characterises the data without much user intervention or interaction. The resultant characterized data can be visualized through graphs, charts, or tables.

Data Discrimination

Data discrimination is a bias when a data set or source is treated differently than others, intentionally or unintentionally. This data mining functionality helps to separate peculiar data sets based on the ambiguity in attribute values.

Prediction

Prediction is among the most popular data mining functionalities determining any missing or unknown element in a data set. Linear regression models based on the previous data are used to make numeric predictions, which help businesses forecast the results of any given event, positively or negatively. There are two types of predictions –

• Numeric Predictions – Predict any missing or unknown element in a data set
• Class Predictions – Predict the class label using a previously built class model

Outlier Analysis

We use the outlier analysis technique if we cannot group data in any class. Outlier analysis helps to learn about data quality. Outlier means data abnormality in most cases. More outliers in your data set low the data quality. You cannot identify data patterns or derive conclusions from data sets with many outliers. The outlier analysis process helps check if any data can be used to analyze after some clean-up. Nevertheless, tracking unusual data and activities is still essential so that any anomalies can be detected beforehand and any business impact can be detected in advance.

Evolution Analysis

Evolution Analysis refers to the study of data sets that may have been through a phase of transformation or change. The evolution analysis models capture evolutionary trends in data, which further contributes to data characterization, classification, or discrimination and clustering for multivariate time series.

Conclusion

Data mining is the most interesting because you can get information without asking specific questions. The process is mainly predictive and uses statistics and algorithms to predict future trends or what can happen from the stored data. Data mining also identifies hidden information in addition to future events. These data mining functionalities contribute toward finding trends in data mining, making it a crucial element of a data scientist’s toolbox.