Data Mining Techniques

Data powers the world - it is the basis for information, analysis, technology, innovation, and decision-making, amongst many other things.

In a business, data facilitates and improves decision-making associated with market trends, operational performance, sales analysis, understanding customer behavior, etc.

All industries worldwide work with data in various forms, from healthcare to finance, IT, marketing, etc. This data is processed to perform numerous tasks. This article will explore data mining and the various techniques involved.

Let’s dive in!

When we see or hear the term mining we think of gemstones, metals, coal, gold, etc., and wonder, what has mining got to do with data? Well, Data mining involves searching and analyzing a huge amount of raw data to identify patterns and extract information that can be beneficial for predicting outcomes.

The nature of collecting and dissecting data has transformed over time. The ancient tally systems we read about in the history books, which employed physical markings to represent quantity, and the use of various forms of tablets which was a method of record record-keeping dating back thousands of years are prime examples.

We have gone through different evolutions from statistical methods used for analyzing data in the 17th/18th century to more automated data processing with the introduction of computers, the internet, and digital technologies for collecting, storing, and sharing data in large quantities.

And in recent times, there have been numerous digital data platforms, and online database systems with real-time access to information, capable of analyzing and processing data. E.g. GoogleBigQuery, Microsoft Azure Synapses Analytics.

Different types of data can be mined, and the techniques can be applied to different data types including but not limited to;

• Data in databases: Data Management System or Relational Data Base System (RDBMS) stores data that are related to each other. This management system has software programs used to manage data.

The Database is simply a set of tables with rows (tuples) and columns (attributes). While mining the database, we can search for trends or data patterns, e.g. Increase and decrease of sales in a business

• Data in the data warehouse: A Data warehouse is a repository or storage location that collects and manages data from multiple sources. This data is queried and analyzed for decision-making purposes or to support business analytics.

Also, it is stored in a multidimensional structure or data cube which means it is organized and represented in multiple dimensions e.g. time, location, product, etc.

• Transactional data: Transactional data are stored in a database that records transactions for actions like purchases, orders, user clicks, etc.

• Other Data Types: Other types of data include; sequence data e.g. stock market data, spatial data e.g. maps, multimedia e.g. audio, web data e.g. web page related, engineering design data e.g. ICs, graph data, etc.

Data mining plays an important role in our world today. Here are some important characteristics of data mining;

• Data Predictions: Data mining facilitates the prediction of future trends this means past data can be used to visualize how things may progress in the future. It can help businesses build models that show future outcomes based on past data that can be used for risk assessment, risk mitigation, fraud detection,etc

• Data Relationship Identification: Data mining is used to find relationships between data; for example, it could be used to identify an area that receives the same amount of consistent visits from middle-aged citizens to answer the question of why.

• Risk Management: Data mining techniques can detect fraudulent activities and identity threats or potential threats to a business. This can help businesses implement techniques to improve cyber security or adopt real-time monitoring techniques to monitor all business activities.

• Customer Satisfaction: Data mining techniques can be used to analyze customer feedback from surveys, reviews, social media, etc which can identify issues, and areas for improvement allowing businesses to take steps to improve customer satisfaction and loyalty.

While collecting and sourcing from diverse sources for an informed decision-making process, it is important to be aware of Data mining’s limitations which include;

• Complex Tools: Data mining tools require a specialist to use them effectively this means that companies or businesses would have to hire the services of a data analyst or train specific staff to analyze data.

• Privacy Concerns: Data can be used to target individuals for marketing purposes and a lot of people are concerned about the safety of their information.

• Inaccurate Results: Data mining can produce inaccurate results because it is not always 100% accurate.

• Resource Intensive: Data mining may require large computational resources and time to analyze data sets accurately and efficiently. Some businesses/organizations may not have the infrastructure or resources to handle data mining.

• Data Quality: Data that’s being analyzed must be of good quality, If data has missing values, errors, inconsistencies, or isn’t prepared properly the result may be completely inaccurate.

We discussed the importance and limitations of Data mining but what steps are typically involved when mining data?

1. Setting Data Objectives: During this stage, the data scientist and shareholders define a problem where data mining would be applied.

2. Data Preparation: This stage involves identifying the data or set of data that will answer questions related to the business. Also, the data is cleaned to remove duplicates, missing values, and outliers.

3. Data Application: Data is applied through mining algorithms to discover relationships, and applying deep learning.

4. Data Evaluation: This step involves assessing the quality and relevance of data before it's interpreted to ensure that the interpretations are valid, understandable, and useful.

Data mining is possible via numerous algorithms and techniques to turn large amounts of data into useful information - these techniques are designed to extract specific types of information from data. Here are some of the various techniques involved in data mining;

1. Association: This method involves finding the relationship and constancy between variables in a given data set. A simple association is made between two or more items often of the same type to identify patterns. The association rule as it is called is used in market analysis for example - a business might use the association rule to identify customers buying behavior. E.g. A customer who buys bread always tends to buy peanut butter.

2. Classification: This technique involves the identification of data into categories or classes. This classification can be based on features or attributes used to build predictive models that would then be used to classify new data based on their features. e.g. customer churn prediction - analyzing customers' engagement patterns, and billing history classification mining can help identify at-risk clients.

3. Clustering: This method involves grouping individual pieces of data to form a consistent structure and identify similarities and patterns in the data. There are different types of clustering algorithms e.g. K-means, hierarchical clustering, and density-based clustering. The choice of algorithm, the similarity measures, number of chosen clusters would determine the quality of the clustering result.

4. Prediction: The prediction mining technique aims to analyze past instances to predict an event. The prediction mining technique uses regression which can used to model the relationship between one or more independent variables and dependent variables. The aim is to build a model that can be used to predict the value of the dependent variable based on the independent variable. The dependent variable is the response variable, while the independent variable is the predictor variable. There are two main types of linear regression and they are simple linear regression and multiple linear regression. Regression is commonly used in demand forecasting, price optimization, trend analysis, etc.

Data generation is a constant process driven by the demands of technology for reasons that may include; business analysis, artificial intelligence, clinical research, and so on.

Subsequently, data is then processed and queried to ascertain outcomes, make predictions, or provide diagnostic analysis. This is where data mining is required, and it often involves extensive data sets. The data mining process differs, depending on the industry, type of data, and the objective of the analysis.

Through this process of learning and discovering with this knowledge, organizations can help identify key attributes of businesses and industries and give incredible insights that can help shape their future.