Businesses are recognizing the importance of data to better understand the inner and outer mechanisms of their company. One method in which this is being done is through mining. The data mining process explores and analyzes information to uncover patterns or rules that are meaningful to an organization. Data mining techniques help to create machine learning models that enable artificial intelligence applications. Let’s take a look at some of these techniques that allow businesses to depend on algorithms for optimized queries and better business decisions.
One of the greatest goals of data-mining is creating more predictable outcomes through standardized algorithms and better formatting. With linear regression, a business can predict a continuous variable’s values with the help of inputs. This method is often used in the real estate sector to predict home values based on information like square footage, year of construction, and ZIP code location in a large volume of data.
Through logistic regression, inputs are used to predict the probability of a categorical variable. This is commonly seen in the banking industry, relying on statistical analysis to determine an applicant’s chances of being approved for a loan based on their credit score, income, gender, age, and other factors. This allows for a predictive model markup language, linking the amount requested on a loan primarily to income and credit score.
In time series-based data mining techniques, forecasting tools focus on the use of time as the fundamental independent variable. Retailers rely on this model to be able to predict the demand for products seasonally, or even monthly, and adjust their inventory accordingly. This provides quick insights into the products that are moving at one location and not another, allowing vendors to use inference to adjust their stock accordingly.
Classification trees, or regression trees, are predictive modeling techniques where the value of both categorical and continuous target variables can be predicted. This model creates a binary rule based on predicted data sets to classify and group the largest proportion of target variables for potential outcomes. With these rules, new groups that are created within data virtualization become predictions in new data. This is important in the insurance sector to understand expanding coverage in different realms. This allows for stronger models to determine risk based on location for homeowners or based on preexisting conditions for patients.
Neural networks are designed to work in a manner similar to how the human brain functions. Just like stimuli in the brain, neural networks use inputs with a threshold requirement. These inputs will ‘fire’ or ‘not fire’ its node based on magnitude. These signals combine with other such responses that may be hidden in the multiple layers of the network. The neural network process goes on repeating through recommendation engines such as those on social media. The benefit is a near-instant set of data to be grasped through analytics.
K-nearest neighbor relies on past observations to categorize new ones. Rather than predictive models, this is driven by data. There are no underlying assumptions made in data exploration before proceeding with this regimen. Complex processes are not a part of examining these databases. New observations are classified by identifying the closest K-neighbors and assigning the majority value. This is crucial in analyses of automation to better address outstanding issues within a supply chain from vendor to customer.
Finally, unsupervised learning has become part of the list of data mining techniques that deliver relevant results to different business sectors. This is where underlying patterns are observed based on data from unsupervised tasks. This includes personalized recommendations for better customer interaction. This is commonly seen in online shopping when making suggestions for customers to buy other products based on customer behavior and their shopping cart at the time of purchase.