The term Big Data is gaining immense popularity. It means huge amounts of data, rich in insights, that can provide value to an organization. There are multiple techniques that can be followed to process data and Data Mining is one of them.
Data Mining refers to the process of converting raw data into valuable insights by running software solutions to find patterns in batches of data. The Classification Technique is one such Data Mining technique that helps in Clustering the data into similar categories based on various parameters.
This article will provide you with a comprehensive guide on Data Mining, Data Mining Classification, Classification Applications in Data Mining, and many more.
Table of Contents
What is Data Mining?
Data Mining is the process of discovering and identifying new patterns from Big Data or large amounts of enterprise data. It is also known as KDD – Knowledge Discovery in Data. The rate of adoption of Data Mining techniques has increased in the past couple of years.
Data Mining helps organizations to leverage data in order to make decision-making more valuable than traditional methods. The Data Mining process helps in gaining insights that define the pathway an enterprise has to take regarding its campaigns, products, locations, and a lot more aspects. Data Mining has two main types: It can either work on the target dataset to describe parameters or predict the outcomes by employing the Machine Learning models.
With the advancement in software solutions, Artificial Intelligence is being used to expedite information. But even as the technology improves, the scalability issues still remain, and mining the data becomes a lot more difficult but at the same time important.
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What is Data Mining Classification?
Data Mining Classification is a popular technique where the data point is classified into Different Classes. It is a supervised learning technique where the quality of data can be changed based on previous data.
The Data Mining Classification Algorithms create relations and link various parameters of the variable for prediction. The algorithm is called the Classifier and the observations are called Instances. Classification helps in determining if the instance is useful to the organization or not.
A Data Mining Classification example can be that of a bank giving loans. There is a master database with the details of all the account holders. Classification helps in categorizing this master database into the probability of loan takers as high, mid, and low so that the bank can determine whom to spend time on so as to meet the target.
What are the Classification Applications in Data Mining?
The classification in Data Mining has many applications in day-to-day life. A few Classification Applications in Data Mining are:
- Product Cart Analysis on the eCommerce platform uses the classification technique to associate the items into groups and create combinations of products to recommend. This is a very common Classification Applications in Data Mining
- The weather patterns can be predicted and classified based on parameters such as temperature, humidity, wind direction, and many more. These Classification Applications of Data Mining are used in daily life.
- The public health sector classifies the diseases based on the parameters like spread rate, severity, and a lot more. This helps in charting out strategies to mitigate diseases. These Classification Applications of Data Mining help in finding cures.
- Financial institutes use classification to determine the defaulters and help in determining the loan seekers, and other categories. These Classification Applications in Data Mining helps in finding the target audience much easier.
Key Languages used for Data Mining
- Python Programming Language: Python is one of the most adaptable programming languages, that is efficient in performing operations ranging from Data Mining, Web Development, Application Development, creating Embedded Systems, and many more all under a single platform. The Pandas library in Python helps in Data Analysis, processing datasets, visualizing using histograms and performing operations on data efficiently. This library is also used to mine data.
- R Programming Language: R has wide support for operations like Data Manipulation, Data Calculations, and Data Visualization. R is also suitable for all operations as it has the provision to implement all the Machine Learning algorithms swiftly. It also has the provision for various statistical and graphical techniques such as Linear Modelling, Non-Linear Modelling, Time-series analysis, Classification techniques, and many more.
- SQL (Structured Query Language): SQL is the language that was designed to maintain and query the data stored inside Relational Database Management Sytems. SQL allows operations like Insertion, Deletion, Updation, and Retrieval of data present in the database. Also, operations like aggregation, max, min, and many more can be applied to the data.
Best Tools for Data Mining
There are many tools available in the market that can perform efficient Data Mining Classification, a few are mentioned below:
1) Oracle Data Mining
Oracle provides an Enterprise Edition for its Database that includes an Oracle Data Mining Tool prebuilt. This tool can easily combine with Oracle Database to perform Data Analysis with ease. This eliminates the requirement of transportation of data into specialized servers. The ODMs help in mining data to identify patterns, and form valuable insights. The ODM can asynchronously process Data Pipelines.
The RapidMiner is a Predictive Analytical tool that is based on Java. This tool is proficient in performing Deep Learning, Text Mining, and Predictive Analytics, under a single platform. It provides both on-premise solutions as well a Cloud framework. The templates employed reduce errors and increase efficiency by reducing delivery times.
3) SAS Enterprise Miner
SAS stands for Statistical Analysis System. This provides Enterprise Miner software that has prebuilt tools and proficiency in Data Mining and Data Optimization. The methodologies employed by the software boost the organization’s goals. The models incorporated in the tool are Descriptive Modeling, Predictive Modelling, and Prescriptive Modeling. The Scaling of the system is handled by Distributed Memory Processing.
4) IBM SPSS Modeler
IBM provides a cutting-edge software solution that offers an enterprise-wide solution. IBM SPSS Modeler is a solution that offers Visual Data Science and Machine Learning tools. This tool is proficient in Data Preparation, Predictive Analysis, and Data Mining Deployment Operations. It also combines the governance and security needs of the organization under the same platform.
What are the Data Mining Classification Techniques?
Data Mining has two main types of Classification Categories available:
Now let us understand the two Data Mining Classification categories in detail.
1) Generative Classification
These Data Mining Classification Algorithm models the distribution of Individual Classes and learns from the model that generates data through estimations and assumptions. The Generative Classification algorithm is used to predict the data that is unseen.
An example of a Generative Data Mining Classification Algorithm is the Naive Bayes Classifier.
Example: Naive Bayes Classifier – Detecting Spam emails by looking at the previous data.
2) Discriminative Classification
The Discriminative Data Mining Classification algorithm is a basic Classifier that determines classes for the entire rows of the data. The classes are decided based on the data quality.
An example of a Discriminative Classifier is Logistic Regression.
Example: Logistic Regression – Acceptance into university based on student grades and test results.
What are the steps involved in Data Mining Classification?
Step 1: Learning Phase
This phase of Data Mining Classification mainly deals with the construction of the Classification model based on different algorithms available. This step requires a training set for the model to learn. The trained model gives accurate results based on the target dataset. When the test data is added to the model it provides accuracy to the Classification Model created.
Step 2: Classification Phase
This phase of Data Mining Classification deals with testing the model that was created by predicting the class labels. This also helps in determining the accuracy of the model in real test cases.
6 Best Classifiers for Mining Data/Data Mining
- Linear Regression
- Logistic Regression
- Random Forest
- Naive Bayes
- Decision Tree
1. Logistic Regression
Logistic Regression is a statistical method that creates a Binomial Classification for a particular event or class. This model gives the probability of every trial and decides which side of the Binary Classification will move. Logistic Regression also helps in determining multiple independent parameters impacting a single outcome.
Logistic Regression is only viable when the predicted variable is binary and there are no missing values in the target dataset. It also requires all the predictors to be independent of each other.
2. Linear Regression
Linear Regression is a Supervised Learning algorithm that performs simple Regression to predict the values based on the independent variables. To find the value of the dependent variable relation between independent variables.
The main issue with the model is it is highly prone to overfitting, and it is not always feasible to separate data in a linear manner.
3. Decision Trees
This is the most robust Classification Technique for Data Mining. It follows a flowchart similar to the structure of a tree. The leaf nodes hold the classes and their labels. The internodes have a Decision algorithm that routes it to the nearest leaf node. There can be multiple internal nodes to do this. The horizontal and vertical phases can be prediction boundaries.
The only challenge is that it is complex, and requires expertise to create and ingest data into it.
4. Random Forest
As the name suggests this model employs multiple Decision Trees and applies sub-sets to these models. Then an average is taken for all the trees to predict the class accuracy. The subsets created are of the same size as that of the true dataset but the samples are replaced for every subgroup.
It is efficient in reducing overfitting and increasing accuracy. The drawback is, that it is very slow for real-time applications and is highly complex to implement.
5. Naive Bayes
The Naive Bayes Algorithm makes the assumption that every independent parameter will equally affect the outcome and has almost equal importance. It calculates the probability of the event occurring, given that an event has already occurred. Naive Bayes requires smaller training sets to learn. It is faster in predicting when compared to other models.
It is plagued with the poor estimation issue where all the parameters have equal importance. It doesn’t provide results that are true in the real world.
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What are the Advantages of Data Mining Classification?
- Data Mining is cost-effective and very efficient compared to other data applications.
- Data Scientists use Data Mining for information analysis, risk modelling, and product safety.
- Data Mining Classification helps businesses make informed decisions and also analyze huge amounts of enterprise data.
- Data Mining Classification helps financial institutions to help defaulters, loan seekers, and other aspects.
What are the Disadvantages of Data Mining Classification?
- Data Mining done through Data Analytics tools is a complex and challenging task.
- There are privacy concerns when the data is mined.
- The data may become inaccurate, and sometimes there are issues with relevancy.
Data Mining is a leading Data Processing technique that provides a holistic view of raw data. There are various data mining techniques available, that can be chosen based on the data requirements. Data Mining helps organizations stay ahead of the competition by charting plans that are gained from enterprise data. This article provided a comprehensive overview of Data Mining, Data Mining Classification, Classification Applications in Data Mining, and many more.
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