Introduction

Data mining has a vast application in big data to predict and characterize data. The function is to find trends in data science. Generally, data mining is categorized as:

1. Descriptive data mining: Similarities and patterns in data may be discovered using descriptive data mining. Descriptive data mining may also be used to isolate interesting groupings within the supplied data. 

This kind of mining focuses on transforming raw data into information that can be used in reports and analyses. It provides certain knowledge about the data, for instance, count, average. It gives information about what is happening inside the data without any previous idea. It exhibits the common features in the data. In simple words, you get to know the general properties of the data present in the database.

2. Predictive data mining: It is not the present behaviour that is being mined for, but rather predictions about the future. It takes advantage of target-prediction capabilities gained via supervised learning. Classification, time-series analysis, and regression are the subset of data mining techniques that fall under this domain.

This helps the developers in understanding the characteristics that are not explicitly available. For instance, the prediction of business analysis in the next quarter with the performance of the previous quarters. In general, the predictive analysis predicts or infers the characteristics with the previously available data. 

The functionality of data mining is listed below

1. Class/Concept Description: Characterization and Discrimination
2. Classification 
3. Prediction
4. Association Analysis
5. Cluster Analysis
6. Outlier Analysis
7. Evolution & Deviation Analysis

Below are all the data mining functionalities with examples, so that you have an in-depth understanding of how these functionalities are used in the real world to work with data.

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1. Class/Concept Description: Characterization and Discrimination

Data is associated with classes or concepts so they can be correlated with results. Data class/concept description can be explained for data mining functionalities with examples. An example of data mining functionality in the class/concept description can be explained by, for example, the new iPhone model, which is released in three variants to attend to the targeted customers based on their requirements like Pro, Pro max, and Plus. 

Data characterization

When you summarize the general features of the data, it is called data characterization. It produces the characteristic rules for the target class, like our iPhone buyers. We can collect the data using simple SQL queries and perform OLAP functions to generalize the data. 

Attribute- oriented induction technique is also used to generalize or characterize the data with minimal user interaction. The generalized data is presented in various forms like tables, pie charts, line charts, bar charts, and graphs. The multi-dimensional relationship between the data is presented in a rule called characteristics rule of the target class. 

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Data discrimination

Data discrimination is one of the functionalities of data mining. It compares the data between the two classes. Generally, it maps the target class with a predefined group or class. It compares and contrasts the characteristics of the class with the predefined class using a set of rules called discriminant rules. The methods used in data discrimination is similar to data characterisation.

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2. Classification

Classification is probably one of the most important data mining functionalities. It uses data models to predict the trends in data. For example, the spending chart our internet banking or mobile application shows based on our spend patterns. This is sometimes used to define our risk of getting a new loan.

It uses methods like IF-THEN, decision tree, mathematical formulae, or neural network to predict or analyse a model. It uses training data to produce new instances to compare with the one existing. 

IF-THEN: The IF clause of an IF-THEN rule is referred to as the rule antecedent or precondition. The THEN portion of the IF-THEN rule is known as the rule consequent. The antecedent portion of the condition includes one or more attribute tests, which are logically ANDed together. The antecedent and the consequent are used together to make a binary true or false decision.

Decision Tree: Classification Models may be created with the use of Decision Tree Mining, a data mining approach. It constructs tree-like models for classifying data. It’s utilized in the development of data models for forming inferences about classes of objects or numerical values.

Neural Networks: By efficiently transforming unstructured data into usable insights, neural networks are a common tool for successful data mining. Using this method, companies may sift through mountains of data in search of insights about their clientele.

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3. Prediction

Prediction data mining functionality finds the missing numeric values in the data. It uses regression analysis to find the unavailable data. If the class label is missing, then the prediction is done using classification. Prediction is popular because of its importance in business intelligence. There are two ways one can predict data:

1. Predicting the unavailable or missing data using prediction analysis
2. Predicting the class label using the previously built class model.

It is a forecasting technique that allows us to find value deep into the future. We need to have a huge data set of past values to predict future trends.

4. Association Analysis

Association Analysis is a functionality of data mining. It relates two or more attributes of the data. It discovers the relationship between the data and the rules that are binding them. It finds its application widely in retail sales. The suggestion that Amazon shows on the bottom, “Customers who bought this also bought..” is a real-time example of association analysis.

It associates attributes that are frequently transacted together. They find out what are called association rules and are widely used in market basket analysis. There are two items to associate the attributes. One is the confidence that says the probability of both associated together, and another is support, which tells past occurrence of associations.

For example, that is if mobile phones are bought with headphones: support is 2% and confidence is 40%. This means that 2% of the time that customers bought mobile phones with headphones. 40% of confidence is the probability of the same association happening again. 

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5. Cluster Analysis

Unsupervised classification is called cluster analysis. It is similar to the classification functionality of data mining where the data are grouped. Unlike classification, in cluster analysis, the class label is unknown. Data are grouped based on clustering algorithms. 

The objects that are similarly grouped under one cluster. There will be a huge difference between one cluster and the other. Grouping is done to maximizing the intraclass similarity and minimizing the intra class similarity. Clustering is applied in many fields like machine learning, image processing, pattern recognition, and bioinformatics.

Below are a few of the clustering algorithms and a little bit about each one:

• K-means clustering algorithm: The goal of the k-means clustering is to divide the data into k groups so that the members of each cluster have similar characteristics while those of other groups are more dissimilar. When comparing the similarity of two spots, distance is the most important factor. This technique operates on the assumption that data points inside a cluster should have as little variation as possible.
• Gaussian Mixture Model algorithm: K-means suffers from the fact that it requires the data to be presented in a circular fashion. Distance calculations in k-means are based on a circular route, hence non-circular data,i.e. data not based around the centroid, isn’t grouped appropriately. Gaussian mixture models are able to correct this problem. It is not necessary for the data to revolve around a centroid for it to be useful. To fit data of any form, the Gaussian mixture algorithm combines parameters from many Gaussian distributions.
• Mean-shift Algorithm: The mean-shift algorithm works well with photos and other computer-vision tasks. There is no need to provide a starting number of clusters since the algorithm determines it automatically. Data points are iteratively shifted towards the mode. The mode refers to the area with the greatest concentration of data points. Unfortunately, this hierarchical clustering approach does not perform well when dealing with massive datasets.

6. Outlier Analysis

When data that cannot be grouped in any of the class appears, we use outlier analysis. There will be occurrences of data that will have different attributes to any of the other classes or general models. These outstanding data are called outliers. They are usually considered noise or exceptions, and the analysis of these outliers is called outlier mining.

This can be considered as one of the other functionalities of data mining. 

These outliers may be valuable associations in many applications, although they are usually discarded as noise. They are also called exceptions or surprises, and it is significant in identifying them. The outliers are identified using statistical tests that find the probability. Other names for outliers are:

1. Deviants 
2. Abnormalities 
3. Discordant 
4. Anomalies

7. Evolution & Deviation Analysis

With evolution analysis being another data mining functionalities in data mining, we get time-related clustering of data. We can find trends and changes in behavior over a period. We can find features like time-series data, periodicity, and similarity in trends with such distinct analysis.

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Conclusion

Holistically data mining and functionalities find many applications from space science to retail marketing. Data mining functionalities in data mining have allowed data to be interpreted in various ways, drawing observations from datasets to create and design new models having various real-world applications that have helped data science reach further heights.

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