An introduction to data mining
Data mining has become a valuable tool for businesses looking to get ahead in the information economy. In this article, we’ll look at what data mining is, discuss applications of data mining and cover some data mining techniques. We’ll also walk you step-by-step through the data mining process.
What is data mining?
Data mining is the process of extracting patterns and other useful information from large data sets. It’s sometimes known as knowledge discovery in data or KDD. Thanks to the rise of big data and advancements in data warehousing technologies, the use of data mining techniques has grown in recent decades, turning raw data into valuable knowledge that companies can use.
Although technology has advanced to handle substantial datasets, executives still face automation and scalability challenges.
Data mining has improved corporate decision-making through clever data analytics. Data mining techniques can be broadly classified into two categories:
Defining the target dataset
Forecasting outcomes using machine learning methods
These tactics are used to organise and filter data – providing the most important information, from fraud detection to user behaviours, bottlenecks, and even security breaches.
Getting into the realm of data mining has never been easier, and collecting meaningful insights has never been faster – especially when combining data mining with data analytics and visualisation tools like Apache Spark. Artificial intelligence advancements are accelerating the adoption of data mining techniques across industries.
What is data mining used for?
The following are some of the applications of data mining:
To achieve a corporate goal
To answer business or research questions
To contribute to problem-solving
To aid in the accurate prediction of outcomes
To analyse and predict trends and anomalies
To inform forecasts
To identify gaps and mistakes in processes, such as supply chain bottlenecks or incorrect data entry
What are the benefits of data mining?
The benefits of data mining are many and varied. We live and operate in a data-driven society, so gaining as many insights as possible is critical. In this complex information age, data mining gives us the tools to solve challenges and issues. The following are some of the benefits of data mining:
It assists businesses in gathering reliable data
It assists organisations in making well-informed decisions
It is a time- and cost-effective solution when compared to other data applications
It enables organisations to make cost-effective production and operational changes
It aids in the detection of credit issues and fraud
It enables data scientists to quickly evaluate massive amounts of data. Data scientists can then use the data to spot fraud, create risk models, and improve product safety
It enables data scientists to create behaviour and trend forecasts and uncover hidden patterns.
Examples of data mining
The following are common applications of the data mining process:
Retailers analyse purchase patterns to establish product categories and determine where they should be placed in aisles and on shelves. Data mining can also be used to determine which deals are most popular with customers or to boost sales in the checkout line.
Data mining is being used to sift through ever-larger databases and improve market segmentation. It is possible to predict consumer behaviour by analysing the associations between criteria such as customer age, gender, tastes, and more, in order to design tailored loyalty schemes.
In marketing, data mining predicts which consumers are most likely to unsubscribe from a product, what they typically search for online, and what should be included in a mailing list to increase response rates. It can play a valuable role in any digital marketing strategy.
Certain networks use real-time data mining to gauge their online television (IPTV) and radio viewership. These systems capture and analyse anonymous data from channel views, broadcasts, and programmes on the fly.
Data mining enables networks to provide personalised recommendations to radio and television listeners and viewers, as well as providing real-time data on customer interests and behaviour. Networks also acquire vital information for their marketers, who can use this information to better target their future customers.
Data mining allows for more precise diagnosis. It is possible to provide more effective therapies when all the patient’s information is available – such as medical records, physical examinations, and treatment patterns. It also allows for more effective, efficient, and cost-effective administration of health resources by detecting risks, predicting illnesses in specific segments of the population, and forecasting hospital admission length.
Data mining in medicine also has the benefit of detecting anomalies, as well as developing better relationships with patients through a better understanding of their requirements.
Data mining is used by banks to better understand market risk. It is often used to analyse transactions, purchasing trends, and client financial data. Data mining also enables banks to gain a better understanding of our online tastes and behaviours in order to improve the return on their marketing initiatives, analyse financial product effectiveness, and ensure regulatory compliance.
The data mining process
The CRISP-DM (Cross-Industry Standard Process for Data Mining) is the most widely used data-mining framework. The CRISP-DM procedure is divided into six stages: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment.
These phases are tackled in sequential order as the process is iterative, which means that any models and understanding developed during the process are designed to be enhanced by subsequent knowledge gathered throughout the process.
1. Business Understanding
The first stage of CRISP-DM is to obtain a thorough understanding of the business and to determine the organisation’s specific needs or goals. Understanding a business means determining the issues the company wishes to address – for example, a company may want to boost response rates for various marketing efforts.
One of the first responsibilities in the Business Understanding phase is to dig down to a more specific definition of the problem. The query could be narrowed to determine which client subsets are most likely to make repeat purchases, or how much they are willing to spend.
2. Data Understanding
Following the definition of the organisation’s goals, data scientists start discovering what exists in the current data. A corporation may have information about a client’s (or potential client’s) name, address, and other contact information. They may also have records of previous purchases.
There may be information about client interests or family makeup, depending on the source of the data. All of this data may be very useful in future campaigns.
3. Data Preparation
Once we have a firm grasp on what data exists and what data does not, the data is prepared and processed in a way that makes it valuable. The data preparation procedure is lengthy and accounts for roughly 80% of the project’s time.
The creation of a data dictionary is the first step in the data preparation process. The data is separated into chunks, then the elements of metadata are described in a way that makes it human-readable to ensure that it is understandable, especially to someone who isn’t a data scientist.
Data analytics is the next part of the data preparation process and involves finding and developing new data points that may be calculated from existing inputs. Helpful profiles can be created using business analytics, which can subsequently be used for predictive modelling and to develop well-targeted marketing campaigns.
The information gathered during data preparation is then used to develop various behavioural models. For example – in the case of marketing campaigns, modelling involves the creation of “training data” representative of the ideal customer.
These consumer profiles are then used as models for scaling campaign success through modelling. Modelling often involves the use of artificial intelligence.
It’s critical to provide clear visual reporting as information is processed, to really understand results on a cognitive level. Graphical presentation techniques are becoming increasingly important for not just comprehending but also recognising trends.
By itself, a stream of data may not appear to be significant, but when displayed on a graph, trends can be quickly discerned. There are a variety of useful tools that will rapidly generate visual reports, such as bar charts and scatter plots.
CRISP-DM is iterative by definition. Each stage not only informs the next one but also the one before it. New information is applied to previous phases as it is learned, and the models are informed and re-informed by each step of the process.
New data points emerge when the data is prepared; these improve when more models are developed and assessed. The results of “final” deployments can be transformed into new models for testing and assessment in the future.
Different data mining techniques
When moving through the six CRISP-DM stages, data scientists rely on a variety of techniques. These include:
Learning to discover patterns in your data sets is one of the most basic data mining techniques. This is frequently an identification of a periodic anomaly in the data or the ebb and flow of a particular variable through time.
For example, you may find that sales of a particular product increase immediately before the holidays, or that warmer weather sends more visitors to your website.
Prediction is one of the most important data mining methods, as it’s used to forecast the types of data you’ll see in the future. In many circumstances, simply noticing and understanding previous patterns is sufficient to provide a reasonable prediction of what will occur in the future. For example, you may look at a consumer’s credit history and previous transactions to see if they’re a credit risk in the future.
Association determines links between different variables. In this situation, you’ll look for certain events that are linked with one another; for example, you might discover that when your consumers buy one thing, they frequently buy another, related item. This is commonly used to populate “people also bought” sections on online stores.
Classification is an advanced data mining technique that requires you to group together diverse attributes into discernible groups, which you can then use to make additional conclusions or perform a specific job.
You might be able to designate individual consumers as “low,” “medium,” or “high” credit risks based on data about their financial backgrounds and buying history. These classifications might then be used to learn even more about those clients.
Clustering is similar to classification, in that it involves putting together groups of data based on their commonalities. For example, you may group different demographics of your audience into distinct categories based on their discretionary income or how frequently they purchase at your store.
In many circumstances, simply finding the overall pattern will not provide you with a complete picture of your data. You must also be able to spot anomalies, sometimes known as outliers, in your data.
If, for example, your customers are nearly all male but there’s a significant rise in female customers during one week in July, you’ll want to research the spike and figure out what caused it so that you can either reproduce it or better understand your audience.
Regression is a type of planning and modelling that is used to determine the probability of a particular variable, given the presence of other variables. You may use it, for example, to forecast a price based on other criteria such as availability, consumer demand, and competition. The main goal of regression is to help you figure out the relationship between several variables in a data set.
How to get started with data mining
Data mining and data science are best learned by doing, so start studying data as soon as possible. However, you’ll also need to study the theory to develop a solid statistical and machine learning foundation to understand what you’re doing and glean valuable insights from the noise of data.
Learn R and Python. These are the most popular languages for data mining.
Take a course. A course will go more in-depth on the points summarised here. FutureLearn offers courses on data mining, such as Data Mining with Weka.
Learn data mining software suites such as KNIME, SAS and MATLAB.
Participate in data mining competitions, such as Bitgrit and Kaggle.
Interact with other data scientists via groups and social networks. Browse the Reddit data mining thread, and attend conferences such as the IDCM.
The world as we know it simply wouldn’t exist without data mining – it’s vital to the world economy, determining everything from what products companies offer to which songs are played on the radio. Learn this important skill today with the range of data science courses offered at FutureLearn.