Data Mining vs. Predictive Analytics – Are They the Same?
Predictive analytics is currently one of the most important Big Data trends. But both predictive analytics and data mining attempt to make predictions about possible events in the future with the help of data models. What are the differences between them?
“Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.” (Wikipedia)
The classical data mining techniques include:
- Data clustering – The aim here is to segment data and to form various groups
- Data classification – Data elements are automatically assigned to different predefined groups/classes
- Regression analysis – relations between (more) dependent and independent variables are identified
- Association analysis – search for patterns in which an event is connected to another event; the dependencies between the data sets are described on if-then rules.
“Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.” (Wikipedia)
This article originally appeared on ecmapping.com. To read the full article, click here.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT and many more.
The Nastel i2M Platform provides:
- Secure self-service configuration management with auditing for governance & compliance
- Message management for Application Development, Test, & Support
- Real-time performance monitoring, alerting, and remediation
- Business transaction tracking and IT message tracing
- AIOps and APM
- Automation for CI/CD DevOps
- Analytics for root cause analysis & Management Information (MI)
- Integration with ITSM/SIEM solutions including ServiceNow, Splunk, & AppDynamics