Businesses collect and store an unimaginable amount of data, but how do they turn all that data into insights that help them build a better business? Data mining, the process of sifting through massive amounts of data to identify hidden business trends or patterns, makes these transformational business insights possible.
Data mining is not a new technology. Its roots have been traced to the 1930s, according to Hacker Bits. Still, the term became more widely used in the 1990s as businesses attempted to grapple with the ever-increasing amount of data our society was producing to derive value from it.
The advent of modern computers and the application of data mining techniques meant businesses could finally analyze exponential amounts of data and extract non-intuitive, valuable insights, forecasting likely business outcomes, mitigating risks, and taking advantage of newly identified opportunities.
Due to its usefulness across many industries and its critical role in business success, data mining is a promising career path. Companies need data scientists skilled in mining techniques who can present their findings in understandable ways.
So what are the key techniques that aspiring data miners should know? Here are 10 data mining techniques that we will explore in detail:
Clustering
Clustering is a technique used to represent data visually — such as in graphs that show buying trends or sales demographics for a particular product.
What Is Clustering in Data Mining?
Clustering refers to the process of grouping a series of different data points based on their characteristics. By doing so, data miners can seamlessly divide the data into subsets.
Methods for Data Clustering
- Partitioning method: This involves dividing a data set into a group of specific clusters for evaluation based on the criteria of each individual cluster. In this method, data points belong to just one group or cluster.
- Hierarchical method: With the hierarchical method, data points are a single cluster grouped based on similarities. Those newly created clusters can then be analyzed separately from each other.
- Density-based method: A machine learning method where data points plotted together are further analyzed, but data points themselves are labeled as a “noise” and discarded.
- Grid-based method: This involves dividing data into cells on a grid. As a result, grid-based clustering has a fast processing time.
- Model-based method: In this method, models are created for each data cluster to locate the best data to fit that particular model.
Examples of Clustering in Business
Clustering helps businesses manage their data more effectively. For example, retailers can use clustering models to determine which customers buy particular products, on which days, and with what frequency. This can help retailers target products and services to customers in a specific demographic or region.
Clustering can help grocery stores group products by various characteristics (brand, size, cost, etc.) and better understand their sales tendencies. It can also help car insurance companies that want to identify customers. In addition, banks and financial institutions might use clustering to understand better about their customers as well.
Association
Association rules are used to find correlations, or associations, between points in a data set.
What Is Association in Data Mining?
Data miners use association to discover unique or interesting relationships between variables in databases. Association is often employed to help companies determine marketing research and strategy.
Methods for Data Mining Association
Two primary approaches using association in data mining are the single-dimensional and multi-dimensional methods.
- Single-dimensional association: This involves looking for one repeating instance of a data point or attribute. For instance, a retailer might search its database for the instances a particular product was purchased.
- Multi-dimensional association: This involves looking for more than one data point in a data set. That same retailer might want more information than what a customer purchased — such as their age, and method of purchase (cash or credit card).
- Examples of Association in Business
The analysis of impromptu shopping behavior is an example of association. From data studies, retailers notice that parents shopping for childcare supplies are more likely to purchase specialty food or beverage items during the same trip.
Association analysis carries many other uses in business. For retailers, it’s particularly helpful in making purchasing suggestions. For example, if a customer buys a smartphone, tablet, or video game device, association analysis can recommend related items like cables or applicable software.
In addition, the government uses associations to plan for public services and use census data.
Data Cleaning
Data cleaning is the process of preparing data to be mined.
What Is Data Cleaning in Data Mining?
Data cleaning involves organizing, eliminating duplicate or corrupted data, and filling in any null values. When this process is complete, It is possible to gather and analyze the most important data..
Methods for Data Cleaning
- Verifying the data: This involves checking that each data point in the data set is in the proper format (e.g., telephone numbers).
- Converting data types: This ensures data uniform across the data set. For instance, numeric variables only contain numbers, while string variables can contain letters, numbers, and characters.
- Removing irrelevant data: This clears useless or inapplicable data so full emphasis can be placed on necessary data points.
- Eliminating duplicate data points: This helps speed up the mining process by boosting efficiency and reducing errors.
- Removing errors: This eliminates typing, spelling, and input errors that could negatively affect analysis outcomes.
- Completing missing values: This provides an estimated value for all data and reduces missing values, which can lead to skewed or incorrect results.
Examples of Data Cleaning in Business
According to Experian, 95 percent of businesses have been impacted by poor data quality. Working with incorrect data wastes time and resources increases analysis costs, and often leads to faulty analytics.
Ultimately, no matter how great their models or algorithms are, businesses suffer when their data is incorrect, incomplete, or corrupted.
Businesses looking for a competitive advantage often find data among their best resources, and data mining techniques are vital in bringing this resource to fruition. Mining allows businesses to harness the power of data, gain insight, detect patterns and anomalies, and find ways to be more productive.
As we continue to produce a growing amount of diverse data, the ability to mine that data for insights will become increasingly important. Organizations generally want faster, more efficient ways to work, more methods to visualize data, and computing systems. As a result, many companies expect to increase their investment in analytics initiatives, which include data mining.