Introduction to What is Data Mining
What Is Data Mining? Data mining is the process of extracting usable insights from colossal amounts of data. It is the process of identifying patterns, trends, and useful data. This in turn allows enterprises to make data-driven decisions from big data. It is even referred to as knowledge discovery of data (KDD).
It’s true that data mining as a vital process in organizations has only gained traction only recently. But its applications have been around for more than a century. Statistical methods like Bayes Theorem and Regression Analysis are some early applications of data mining. The only difference between then and now is the rising volume of data. It helps enterprises in solving problems and evading risks.
Important Techniques in Data Mining
Getting the most out of data involves carrying out some statistical and computational techniques. Using these, businesses are enabled with the power to identify relationships and trends that would otherwise be overseen. Following are some of the functions –
Association rule learning/ Pattern mining
In data mining, it is important to identify relationships and patterns in the dataset. One criterion to do this is by identifying the confidence and support parameters. Support is the frequency of items in the database. Whereas, Confidence determines the accuracy of a particular condition applied to the data.
Example – Typically used in market basket analysis to determine what kind of items are more likely to be purchased together.
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Predictive modelling is used in cases where a result/ insight needs to be determined about a particular group of data. It is based on analysis and segregations done on an existing dataset. Classification is the best use case of predictive analytics. Here, data is already grouped and models are employed. These models help to search for patterns that help distinguish these groups.
Example – Spam filtering to identify whether incoming emails are spam or not using an existing classifier model.
This approach involves the grouping of data after discovering common patterns. It is an unsupervised learning method. It mostly involves clustering i.e dividing data into groups that are not formerly known.
Example – Real estate agencies use clustering to determine which kind of houses are typically bought by people. They determine this based on age group/ credit score/ ethical background. This model can later be used to simplify the sales process in prospective buyers.
Anomaly detection is like a negation of clustering. It uses outlier data to detect unusual items that don’t fit in a specified pattern. Anomaly detection normally focuses on modelling “normal” behaviour. This is done in order to sniff out peculiar transactions.
Example – In detecting fraudulent transactions and withdrawals for a bank customer. Intrusion detection in the monitoring of security systems.
Data mining life cycle
Ideally, a data mining project will begin by finding out problem areas, acquiring quality data to rectify them, and performing EDA (abrv. Exploratory Data Analysis) on said data. Data scientists and domain experts in collaboration perform the following tasks to achieve valuable insights from data –
- Problem understanding and acquiring data
A data scientist needs to inculcate an understanding of business parameters and the current market scenario. It is also important to ask the right questions that the project aims to solve. This further enables them to get proper quality of data for analysis. Remember, the right solution can only come up when good quality data is in the picture.
- Data preprocessing
This technique is used to obtain accuracy, consistency and completeness in the data. The data collected should serve the required purpose. For that reason, data preparation and cleaning methods are used to fill in missing data and exclude noisy data (inconsistencies and outliers).
Different machine learning algorithms can be utilized to detect patterns and gain information from the data prepared. In this step, the data is structured using clustering and regression techniques.
- Visualization and deployment
No project is complete without gaining valuable outcomes and insights. After the modeling process, it is deployed to decision-makers to make the most out of the outcomes. Interactive dashboards, charts, reports are created using data visualization practices.
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The data mining process has many perks but comes with its own set of challenges as well
|Benefits of Data Mining||Disadvantages of Data Mining|
|Enables organisations to obtain insights on their customers.||Invasion of user privacy.|
|Aids in the decision-making process of business ventures||Marketing of customer data accumulated by a business|
|Modelling enables the analysis of a vast amount of data in a short span of time.||Anomalies in detection and poorer success rate of models in actual prediction|
|A cost-efficient way of detecting patterns in data.||Data mining software is complex for operational purposes. The efficiency of data mining hugely relies on the procedures and techniques used.|
Applications of data mining
The purpose of data mining in medicine is to develop a predictive model. Such models provide solid predictions and aids doctors to improve their patients’ diagnosis. Data mining is steadily making its mark in the medical and bioinformatics domain. Some common applications are:
- Genome mapping – to detect anomalies and patterns in DNA to check whether a person is likely to have ailments like Cancer, Alzheimer’s etc.
- Researching efficacy of vaccines and inoculation
- Classification techniques have been set in place at hospitals and medical facilities. These techniques use a patient’s symptoms and history to find out whether they are likely to have Heart disease, TB, Kidney failure etc
Well-known retail platforms like Amazon, Myntra, Flipkart recommend products to drive larger sales revenue. They also investigate the impact of their promotional campaigns.
For example, Amazon noticed that “The Great Indian Festival” generated a humongous amount of sales. They eventually made it an annual affair. Customer buying patterns, ratings and order history can be utilised for sales growth. Data mining can make a huge impact in this sector.
Data mining helps the banking domain to have a better understanding of market risks. It can also be used in credit card fraud detection and identifying likely loan defaulters. Most banks also have systems in place to detect money laundering or suspicious transactions. This ultimately helps the banks in fulfilling customer satisfaction and customer retention as well.
Top software applications for data mining
for the Social Sciences)
|– Machine learning algorithms |
– Statistical analysis (descriptive, regression, clustering, etc.)
– Text analysis
– Integration with big data
|– Immensely popular|
– Automation enabled
– Easy to use graphical interface
– Integration with open source (Python as well as R)
|Sisense||– Ability to join data from different sources|
– Build and share interactive dashboards
– Real-time querying allowed
|– Easy to use|
– Effortless in complex data querying
– Can be integrated with IoT
|RapidMiner||– Open-source can be combined with R and Python|
– Unified platform for data clustering, filtering, merging and joining.
|– The best tool dedicated to machine learning and data mining|
– Visual workflow design
What lies in store for the future
We have already discussed the many applications of data mining. In 2020, it was estimated that each person around the globe generates 1.7 MB of data every second. Wearable OS, IoT appliances, OTT platforms, Google search – you name it. Everything is churning information by the second.
With huge data servers being used across industries in all domains, data mining shows great potential. Cloud-based technologies have also started employing machine learning and AI techniques. Data mining has tremendous scope in the future. “Data is the new oil” is not merely a quip of the technological world. Mining data has become an essential means for all business ventures.