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A Comprehensive Guide to Data Mining: Techniques, Tools and Application

A Comprehensive Guide to Data Mining: Techniques, Tools and Application

Introduction

Sifting through very large amounts of data for useful information. Data mining uses artificial intelligence techniques, neural networks, and advanced statistical tools. It reveals trends, patterns, and relationships, which might otherwise have remained undetected. In contrast to an expert system, data mining attempts to discover hidden rules underlying the data. Also called data surfing.

In this blog, we will be presenting a comprehensive detail about data mining. Additionally, this blog will help you to get into the details of data mining. Furthermore, it will help you to get the complete picture in one place!

 

What is Data Mining?

Data mining is not a new concept but a proven technology that has transpired as a key decision-making factor in business. There are numerous use cases and case studies, proving the capabilities of data mining and analysis. Yet, we have witnessed many implementation failures in this field, which can be attributed to technical challenges or capabilities, misplaced business priorities and even clouded business objectives. While some implementations battle through the above challenges, some fail in delivering the right data insights or their usefulness to the business. This article will guide you through guidelines for successfully implementing data mining projects.

Also, data mining is the process of uncovering patterns inside large sets of structured data to predict future outcomes. Structured data is data that is organized into columns and rows so that they can be accessed and modified efficiently. Using a wide range of machine learning algorithms, you can use data mining approaches for a wide variety of use cases to increase revenues, reduce costs, and avoid risks.

Also, at its core, data mining consists of two primary functions, description, for interpretation of a large database and prediction, which corresponds to finding insights such as patterns or relationships from known values. Before deciding on data mining techniques or tools, it is important to understand the business objectives or the value creation using data analysis. The blend of business understanding with technical capabilities is pivotal in making big data projects successful and valuable to its stakeholders.

 

Different Methods of Data Mining

Data mining commonly involves four classes of tasks [1]: (1) classification, arranges the data into predefined groups; (2) clustering, is like classification but the groups are not predefined, so the algorithm will try to group similar items together; (3) regression, attempting to find a function which models the data with the least error; and (4) association rule learning, searching for relationships between variables.

1. Association

Association is one of the best-known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction. That’s is the reason why the association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently purchase together.

Retailers are using association technique to research customer’s buying habits. Based on historical sale data, retailers might find out that customers always buy crisps when they buy beers, and, therefore, they can put beers and crisps next to each other to save time for the customer and increase sales.

2. Classification

Classification is a classic data mining technique based on machine learning. Basically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. Classification method makes use of mathematical techniques such as decision trees, linear programming, neural network, and statistics. In classification, we develop the software that can learn how to classify the data items into groups. For example, we can apply classification in the application that “given all records of employees who left the company, predict who will probably leave the company in a future period.” In this case, we divide the records of employees into two groups named “leave” and “stay”. And then we can ask our data mining software to classify the employees into separate groups.

3. Clustering

Clustering is a data mining technique that makes a meaningful or useful cluster of objects. These objects have similar characteristics using the automatic technique. Furthermore, the clustering technique defines the classes and puts objects in each class. But classification techniques, assignes objects into known classes. To make the concept clearer, we can take book management in the library as an example. In a library, there is a wide range of books on various topics available. The challenge is how to keep those books in a way that readers can take several books on a particular topic without hassle. By using the clustering technique, we can keep books that have some kinds of similarities in one cluster or one shelf and label it with a meaningful name.

4. Regression

In statistical terms, a regression analysis is a process of identifying and analyzing the relationship among variables. it can help you understand the characteristic value of the dependent variable changes if any one of the independent variables is varied. this means one variable is dependent on another, but it is not vice versa.it is generally used for prediction and forecasting.

 

Data Mining Process and Tools

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a conceptual tool that exists as a standard approach to data mining. The process outlines six phases:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modelling
  5. Evaluation
  6. Deployment

The first two phases, business understanding and data understanding, are both preliminary activities. It is important to first define what you would like to know and what questions you would like to answer and then make sure that your data is centralized, reliable, accurate, and complete.

Once you’ve defined what you want to know and gathered your data, it’s time to prepare your data — this is where you can start to use data mining tools. Data mining software can assist in data preparation, modelling, evaluation, and deployment. Data preparation includes activities like joining or reducing data sets, handling missing data, etc.

The modelling phase in data mining is when you use a mathematical algorithm to find a pattern(s) that may be present in the data. This pattern is a model that can be applied to new data. Data mining algorithms, at a high level, fall into two categories — supervised learning algorithms and unsupervised learning algorithms. Supervised learning algorithms require a known output, sometimes called a label or target. Supervised learning algorithms include Naïve Bayes, Decision Tree, Neural Networks, SVMs, Logistic Regression, etc. Unsupervised learning algorithms do not require a predefined set of outputs but rather look for patterns or trends without any label or target. These algorithms include k-Means Clustering, Anomaly Detection, and Association Mining.

Data evaluation is the phase that will tell you how good or bad your model is. Cross-validation and testing for false positives are examples of evaluation techniques available in data mining tools. The deployment phase is the point at which you start using the results.

 

Importance of Data Mining

 

1. Marketing / Retail

Data mining helps marketing companies build models based on historical data to predict who will respond to the new marketing campaigns such as direct mail, online marketing campaign…etc. Through the results, marketers will have an appropriate approach to selling profitable products to targeted customers.

Data mining brings a lot of benefits to retail companies in the same way as marketing. Through market basket analysis, a store can have an appropriate production arrangement in a way that customers can buy frequent buying products together with pleasant. In addition, it also helps retail companies offer certain discounts for particular products that will attract more customers.

2. Finance / Banking

Data mining gives financial institutions information about loan information and credit reporting. By building a model from historical customer’s data, the bank, and financial institution can determine good and bad loans. In addition, data mining helps banks detect fraudulent credit card transactions to protect the credit card’s owner.

3. Manufacturing

By applying data mining in operational engineering data, manufacturers can detect faulty equipment and determine optimal control parameters. For example, semiconductor manufacturers have a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are a lot the same and some for unknown reasons even has defects. Also, data mining has been applying to determine the ranges of control parameters that lead to the production of the golden wafer.

4. Governments

Data mining helps government agency by digging and analyzing records of the financial transaction to build patterns that can detect money laundering or criminal activities.

 

Applications of Data Mining

  • There are approximately 100,000 genes in the human body. Each gene is composed of hundreds of individual nucleotides which are arranged in a particular order. Ways of these nucleotides being ordered and sequenced are infinite to form distinct genes. Data mining technology can be used to analyze the sequential pattern. You can use it to search similarity and to identify particular gene sequences. In the future, data mining technology will play a vital role in the development of new pharmaceuticals. Also, it may provide advances in cancer therapies. 
  • Financial data collected in the banking and financial industry is often relatively complete, reliable, and of high quality. This facilitates systematic data analysis and data mining. Typical cases include classification and clustering of customers for targeted marketing. It can also include detection of money laundering and other financial crimes. Furthermore, we can look into the design and construction of data warehouses for multidimensional data analysis. 
  • The retail industry is a major application area for data mining since it collects huge amounts of data on customer shopping history, consumption, and sales and service records. Data mining on retail is able to identify customer buying habits, to discover customer purchasing pattern and to predict customer consuming trends. This technology helps design effective goods transportation, distribution policies, and less business cost.
  • Also, data mining in the telecommunication industry can help understand the business involved, identify telecommunication patterns, catch fraudulent activities, make better use of resources and improve service quality. Moreover, the typical cases include multidimensional analysis of telecommunication data, fraudulent pattern analysis and the identification of unusual patterns as well as multidimensional association and sequential pattern analysis.

 

Summary

The more data you collect…the more value you can deliver. And the more value you can deliver…the more revenue you can generate.

Data mining is what will help you do that. So, if you are sitting on loads of customer data and not doing anything with it…I want to encourage you to make a plan to start diving into it this week. Do it yourself or hire someone else…whatever it takes. Your bottom line will thank you.

Always query yourself how are you bringing value to your business with data mining!

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