What is data mining?
Data mining is the process of revealing hidden patterns in data by finding anomalies, associations and dependencies in large volumes of data to uncover new insight and predict outcomes. The term “data mining” became known in the 1990s. Over time, as the volume of data has grown, free data mining tools and free data mining software have become critical to data science and machine learning with the help of an open-source data mining platform.
Why is it important?
Data mining tools and knowledge discovery is at the core of analytics across industries from manufacturing, to retail, life sciences, pharma, financial services and banking, and more. The massive amounts of data being generated by companies today call for efficient data mining tools for businesses to discern what data is relevant and accelerate and improve the data-driven decision-making process. Simultaneously, businesses, big and small, must ensure they meet security, access, and privacy compliance and utilize their resources appropriately.
How does it work?
Data mining tools can easily process complex and disparate datasets and produce more accurate predictive insights. This enables companies to make better data-driven decisions quickly. Today’s growing volumes of data together with the development of faster computer systems have seen an explosion in the popularity of highly advanced techniques such as artificial intelligence and deep learning–natural language processing, gradient boosting, or reinforcement learning, discrimination free learning and bias removal–to name just a few.
How does KNIME help?
Not only is KNIME one of the few open-source data mining tools in the market, it supports the entire data science lifecycle. This means that the best, sophisticated, state of the art techniques are continuously integrated into your data solutions.
Data-driven businesses can keep their competitive advantage, leveraging KNIME software to efficiently design, train, and apply the most advanced ML methods with enterprise AI. KNIME’s visual programming environment lowers the barriers to applying data mining techniques: business users can start simple and data analysis skills over time.
Data Mining with KNIME in Departments and Industries
Download data mining blueprints from financial services to marketing, ecommerce, life sciences, and more.
Fraud Detection for Financial Services
Fraud detection data is hard to get and real-time responses are hard to achieve. With KNIME, users can apply advanced machine learning and anomaly detection techniques to build successful fraud detection solutions and run flexibly either on-site or in the cloud.
⇒ Learn how to apply classic data mining techniques in the article Fraud detection using random forest, neural autoencoder, and isolation forest techniques
⇒ Download a comparison and examples of fraud detection techniques in KNIME
Risk Assessment for Credit Scoring
A metric lenders use to assess risk of awarding a customer a loan is “probability of default.” Based on this, banks group customers into similar risk bands and determine which interest rate to charge.
⇒ Download a blueprint to see how Risk Scoring can be done. The example draws on the Give me some credit dataset
Churn Prediction for Customer Care
Advanced supervised machine learning algorithms to build classification and predictive models are used effectively by businesses to make critical predictions, for example when a customer is likely to churn and stop purchasing a project or cancel a subscription to a service.
⇒ Read about how to predict customer churn with machine learning in KNIME
⇒ Download a set of examples of classification for predictive modeling on the KNIME Hub
Customer Analysis for Content Marketing
Discovering patterns and relationships in data can provide companies with valuable insight into how to improve sales and marketing techniques. Sentiment analysis is a popular technique to extract knowledge from social media or from customer reviews of products and services.
⇒ Learn how to approach sentiment classification with supervised machine learning algorithm in this Sentiment Analysis Tutorial
⇒ Download a blueprint to build a sentiment predictor using BERT
Topic Analysis for CX Strategy
Businesses deal with huge volumes of unstructured text on a daily basis from emails to support tickets, social media posts or online reviews. Analyzing large amounts of text data manually is time consuming, expensive, and often error prone. Text mining techniques such as decision trees or LDA for topic detection can automate this process and make it more accurate.
⇒ Read about using the LDA algorithm in topic detection to analyze customer feedback on hotel reviews and download a blueprint to generate Topic Models from Reviews
⇒ Download a blueprint that uses decision trees to forecast box-office success of movies with plot summaries
Customer Segmentation for Product Line Management
This data mining technique is undoubtedly one of the most implemented data mining applications. By clustering customers based on revenue creation, loyalty, demographics, buying behavior, or any combination of these criteria, companies can optimize marketing efforts, improve CX, and maximize customer value.
⇒ Read how to set up customer segmentation in a KNIME workflow and refine customer segments with business knowledge
⇒ Download two blueprints for customer segmentation
Market Basket Analysis in eCommerce
Meet the needs of creating personalized offers for customers. Correlations and relationships in your data can point out valuable marketing information.
⇒ Read about how to build a market basket analysis or recommendation engine and produce a set of association rules
⇒ Download a set of solutions for market basket analysis to provide more accurate shopping recommendations to customers
Decision Trees for Business Planning
When should a company expand into new markets or change their product portfolio? When mining data they can be used to evaluate options and help to decide which one to pursue.
⇒ This article discusses the decision tree algorithm and when to use a random forest in a simple sailing example
⇒ Download a blueprint to train a random forest
Cluster Analysis in Clinical Research
From analyzing clinical studies to finding treatment pathways for patients, cluster analysis has become increasingly popular since the development of computers with greater processing power accelerates the processing of this technique.
⇒ For a discussion of k-means clustering, hierarchical clustering, and DBSCAN, read What is Clustering and How Does it Work?
⇒ Download a blueprint for Molecular Clustering
Information Extraction for Drug Purpose Prediction
Keeping track of the latest developments in research is time-consuming. Information Extraction (IE) techniques enable companies to automate the approach.
⇒ Read how information extraction is performed by training a named-entity recognition model to detect and predict the purpose of newly identified drugs
⇒ Download and explore Prediction of Drug Purpose blueprints