Types of Data Mining Techniques

Background

Initially, the amount of data processed in various fields was very low. The computer hardware for data processing was very expensive. The number of operations performed on data was also lower. However, the amount of data started increasing gradually, and the cost of hardware used for data processing started falling. And the number of operations performed on data started increasing.

The first approach was a sophisticated Database Management System (DBMS) to handle this increasing data and operations. However, there were certain drawbacks to DBMS, such as:

• We need to scan the whole database before doing any operations on the data.
• It is a time-consuming process.
• It may not be reliable to handle a huge amount of data.
• Significantly less relevant and less helpful in handling vast amounts of data.

So, there arose a need for a second approach known as “Data Mining.”

Data Mining as a Process of KDD

KDD is the popular short form for the abbreviation of Knowledge Discovery in Databases. The primary goal of the KDD procedure is to extract information from data in huge databases. It accomplishes this by employing different data mining algorithms.

The following phases are used in the entire process of detecting and analysing patterns from data:

1. Data Cleaning: The noisy and inconsistent data is cleaned in this step.
2. Data Integration: Several data sources are merged in this process.
3. Data Selection: This stage retrieves data from the database relevant to the analytical activity.
4. Data Transformation: Using summary or aggregation processes, data is transformed or consolidated into forms suitable for mining in this step.
5. Data Mining: We use intelligent methods to extract data patterns in this step.
6. Pattern Evaluation: We assess different patterns in this step.
7. Knowledge Presentation: We represent knowledge in the appropriate form in this step.

Types of Data Mining

1. Predictive Data Mining: This type of data mining focuses on predicting future outcomes based on historical data. Techniques like regression, classification, and forecasting are commonly used.
2. Descriptive Data Mining: Descriptive data mining aims to identify patterns, relationships, and trends within existing data. Methods like clustering, association, and summarization are used to gain insights.

Data Mining Techniques

Now that we have a clear understanding of data mining, we can move forward to learn about different techniques used for data mining, i.e., extracting useful information or knowledge. from data. Now let’s look at various data mining techniques.
 

(Different data mining techniques)

1. Association Rules
   This data mining method aids in the discovery of a connection between two or more things. In the data set, it identifies a hidden pattern.
2. Classification
   Classification is a data mining technique that allocates objects in a dataset to desired groups or classes. The purpose of classification is to correctly anticipate the target class for each case in the data. A classification model, for instance, might be used to categorise loan applicants as low, medium, or high credit risk.
3. Clustering
   Clustering analysis is a data mining approach for detecting similar data. This technique aids in the recognition of differences and similarities in data. Clustering is similar to classification in that it requires grouping data pieces together based on similarities. Clustering helps in grouping similar data together.
4. Regression analysis
   The term "regression" refers to a data mining approach for predicting numeric values in a data set. Regression can be used to predict the cost of a product or service and other variables.
5. Prediction
   The prediction uses several other data mining techniques, including trends, clustering, classification, and so on, to forecast a future event. It examines prior events or instances in the correct order.
6. Artificial Neural Network
   A neural network is a collection of algorithms that recognise underlying relationships in a batch of data using a method that mimics how the human brain works. The artificial neural network (ANN) handles information in the same manner that the human brain does.
7. Outlier Detection
   This data mining technique is concerned with identifying data elements in a data set that do not match an expected pattern or behaviour.
8. Sequential Patterns
   Sequential pattern is a data mining technique for discovering sequential patterns by examining sequential data. It comprises discovering interesting subsequences.

Now, you have a basic idea of the different techniques of data mining. We will describe a few essential techniques in the next section to understand better how these techniques work.

Association Rule Mining

Association rule mining discovers interesting relationships that exist among large sets of data. This rule indicates how often a transaction's itemset appears. Market-Based Analysis is a typical example of association rule-based mining.

To define association rules in simple words, “The rules of the association are simple ‘If/Then' statements that are used to find relationships between two different data points in a data set.”
Hence, there are two parts of an association rule:

1. An antecedent (if)
2. A consequent (then)

Example: “If a customer buys a laptop, he is 70% likely to buy headphones.”

Methods for Data Mining Association

The single-dimensional and multidimensional methods are the two most common approaches to data mining that use association.

1. Single-dimensional association
   Searching for a single repeated instance of a data item or attribute is known as a single-dimensional association. A store, for example, might scan its database for instances where a specific product was purchased.
2. Multi-dimensional association
   This involves searching a data collection for more than one data point. That same shop might be interested in knowing more about a consumer than just what they bought, such as their age, method of payment (cash or credit card), or age.

Rule Evaluation Metrics

• Support: The frequency with which the if/then connection exists in the database is indicated by support.
• Confidence: Confidence indicates how many times these correlations have been proven correct.
• Lift: This measurement technique assesses the accuracy of the confidence in the frequency with which an item is purchased.

Outlier Detection

Outlier detection is the observation of data objects in a dataset that do not follow a predictable pattern or behave predictably. An outlier is a data point that deviates too much from the rest of the dataset. An outlier exists in the vast majority of real-world datasets. Outliers, novelties, noise, deviations, and exceptions are all terms used to describe anomalies in a dataset. 

Types of outliers

1. Point Outliers
   A point outlier (also known as a global outlier) is a single data point that deviates significantly from the rest of the data points in a dataset. Almost all outlier detection algorithms are designed to locate global outliers.
2. Collective Outliers
   Collective outliers occur when a group of data points in a dataset deviate significantly from the remaining dataset. Individual data objects may not be outliers in this case, but when viewed as a group, they may act as outliers.
3. Conditional Outliers
   If a data object in a dataset deviates significantly from the other data points because of a single context or situation, it is known as a "conditional outlier." In one situation, a data point may be an outlier, yet it may behave normally in another environment.

Clustering

A cluster is a collection of data elements that are similar to one another. That indicates the objects are similar within the same group but differ from one another or are dissimilar or unrelated to the objects in other groups or clusters. Through this method, miners can then easily separate the data into subsets, allowing for more informed decisions on broad demographics.

Methods for Data Clustering

1. Partitioning clustering
   The partitioning approach involves splitting a data set into a collection of distinct clusters for evaluation based on the criteria of each cluster. Data points in this method are assigned to only one group or cluster.
2. Hierarchical clustering
   The hierarchical technique groups data points based on their similarities into a single cluster. We can examine these freshly formed clusters independently of one another.
3. Density-based clustering
   Data points plotted together are further evaluated using a density-based approach; however, data points plotted alone are designated "noise" and removed.
4. Grid-based clustering
   This divides data into grid cells, which may subsequently be grouped by individual cells rather than the full database. As a result, grid-based clustering processes data quickly.
5. Model-based clustering
   This method involves creating models for each data cluster to find the best data to fit that model.

Classification

In data mining, classification is a popular technique for separating data points into different classes. It lets you organise a wide range of data sets, including complex and big datasets as well as small and basic ones.

Are you confused between classification and clustering? Let’s compare both. The categorisation of objects into one or more classes based on features is done using both classification and clustering. They appear to be the same process since the differences are minor. In the case of classification, each input instance has predetermined labels assigned to it based on its properties, whereas in the case of clustering, these labels are not present.

Methods for Data Mining Classification

• Decision Trees
  For the data classification, we can ask some Yes/No questions and map the results in a chart called a decision tree. If a computer business wants to anticipate whether or not a possible buyer will buy a laptop, it can inquire, "Is the potential buyer a student?" We can similarly ask other questions, and then we can sort the data into decision trees.
• K-nearest neighbours (KNN)
  This method compares an unknown object to others to identify it. By computing the distance between the test data and all training points, KNN tries to predict the proper class for the test data. Then choose the K number of points most similar to the test data. The KNN algorithm analyses the likelihood of test data belonging to each of the 'K' training data classes, and the class with the highest probability is chosen.
• Random Forest Classifier
  On diverse dataset sub-samples, the random forest classifier fits multiple decision trees. It makes use of the average to improve forecast accuracy and prevent overfitting. The sub-sample size is always the same as the size of the input sample; however, the samples are generated with replacement.
• Support Vector Machine (SVM)
  The support vector machine algorithm, often known as SVM, depicts training data as a space with significant gaps dividing it into groups. Following that, new data points are mapped into the same space, and their categories are predicted based on which side of the gap they belong to. Because it only uses a subset of training points in its decision function, this technique is particularly beneficial in high-dimensional spaces and is memory-efficient.

Regression Analysis

In data mining, the relationship between one or more predictor variables and a continuous-valued response variable can be modelled using regression analysis. The predictor variables are the attributes of interest, and the response variable is the attribute we want to predict. The Continuous Value Classifier is another name for this classifier. 

Types of Regression Analysis

Linear regression and multiple linear regression are the two types of regression models.

1. Linear regression
   It is often known as simple regression, which establishes the link between two variables. Linear regression is represented graphically as a straight line, with the slope indicating how a change in one variable affects the other.
2. Multiple linear regression: In the case of complicated data relationships, the relationship may be explained by more than one variable. In this scenario, an analyst performs multiple regression, which entails employing more than one independent variable to explain a dependent variable.

Frequently Asked Questions

Is it possible to undertake data mining without a data warehouse?

The simple answer is that data mining can be done without a distributed data warehouse.

In an interview, how do you describe data mining?

Data mining is the process of extracting useable data from a larger amount of raw data using a combination of techniques such as machine learning, statistics, and database systems. It involves using one or more software to analyse data patterns in large batches of data.

What impact will data mining have in the future?

Data mining techniques can forecast future behaviour and trends, allowing firms to make proactive, data-driven decisions. Data mining techniques can provide answers to business questions that were previously too time-consuming to answer.

What do partitioning and clustering imply?

Partitional clustering is a technique that divides observations into several groups depending on their similarity within a data collection. The analyst must define the number of clusters to be formed in the algorithms.

Conclusion

This article has covered the basics of data mining and different data mining techniques used to mine data. Statistical models, machine learning approaches, and mathematical algorithms like neural networks and decision trees can be used as data mining tools.

Different data mining techniques discussed are:

• Classification
• Clustering
• Prediction
• Association Rules
• Regression analysis
• Artificial Neural Network
• Outlier Detection
• Sequential Patterns

We hope this blog has helped you enhance your knowledge of different data mining techniques. If you want to learn more, check out our articles on  "Data Preprocessing," "Orange in Data Mining," "Python in Data Mining," “Anomalies In DBMS,” "Applications of Data Mining" and "Outliers in Data Analysis."

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