SAS Enterprise Miner by SAS Institute
SAS Enterprise Miner is a powerful, high-end data mining and machine learning workbench that allows users to build sophisticated predictive and descriptive models through an intuit...
Data Mining Software
Oracle Data Mining reviews and summary
Oracle Data Mining (ODM) is a powerful, built-in option for the Oracle Database that provides advanced data mining algorithms directly within the database environment. This enables data analysts and scientists to build and deploy predictive models without moving data out of the secure, high-performance Oracle Database. ODM supports a wide range of techniques, including classification, regression, clustering, association rules, and feature selection. Its primary applications include forecasting future trends, identifying new customers' selling interests through affinity analysis, and detecting sophisticated patterns indicative of fraud or anomalies, all while leveraging th... Data scientists, business analysts, and database administrators working with...
Best for
Data scientists, business analysts, and database administrators working within an Oracle Database ecosystem who need to perform advanced predictive analytics and data mining directly on their operational data. It is particularly useful for applications in customer analytics, risk management, and fraud detection where data volume and security are paramount.
Starting price
Pricing not listed
Category
Data Mining Software
Vendor
Oracle
Key takeaways
Our verdict
Oracle Data Mining is a highly efficient and secure option for organizations deeply invested in the Oracle stack. By performing mining inside the database, it eliminates data movement overhead and leverages database performance, making it a potent tool for in-database advanced analytics, though it naturally ties users to the Oracle platform.
Quick facts
Oracle Data Mining at a glance
Overall rating
4.4/5
Reviews
5
Starting price
Pricing not listed
Vendor
Oracle
Founded
1977
Location
United States
Category
Data Mining Software
Ratings
Oracle Data Mining ratings
Ratings in this section summarize available rating data. Software reviews are shown separately when users submit reviews.
Decision notes
Oracle Data Mining pros and cons
Potential strengths
- Clear buyer-fit positioning is available in the profile data.
Points to verify
- Confirm current pricing, contract terms, and included plan details with the vendor.
- Confirm product-specific availability for category-level features before buying.
- There are no written reviews for this software yet.
Buyer fit
Who uses Oracle Data Mining?
Data scientists, business analysts, and database administrators working within an Oracle Database ecosystem who need to perform advanced predictive analytics and data mining directly on their operational data. It is particularly useful for applications in customer analytics, risk management, and fraud detection where data volume and security are paramount.
Feature research
Oracle Data Mining features
These are common features buyers compare in Data Mining Software. Product-specific availability should be confirmed with the vendor.
Automated Information Extraction
Automate the collection of data from web or document sources into structured databases.
Visual Data Reporting
The use of visual elements like charts and graphs to represent complex data sets.
Fraud Prevention Systems
Detect and mitigate potentially fraudulent or unauthorized actions through real-time monitoring.
Linked Data Management
Manage interconnected data from various sources, enhancing data discoverability and integration.
Predictive ML for Business
Deploy predictive algorithms to analyze key business metrics like revenue and sales trends.
Predictive Modeling
Utilize statistical techniques to forecast future outcomes based on historical data patterns.
Semantic Search
Utilize artificial intelligence to understand query intent and contextual meaning for precise results.
Advanced Statistical Modeling
Utilize mathematical and statistical modeling to derive insights from data.
Text Mining
Extract valuable insights and patterns from unstructured text data using advanced analytical techniques.
Pricing
Oracle Data Mining pricing
Starting price
Pricing not listed
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Pricing model: Per Feature
Pricing can change. Confirm current plans and terms with the vendor.
Compare
Oracle Data Mining alternatives
Compare Oracle Data Mining with other Data Mining Software tools that buyers often evaluate.
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limestats by limestats
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IBM Process Mining by IBM
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Software reviews
Oracle Data Mining software reviews
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FAQ
Oracle Data Mining FAQs
Oracle Data Mining (ODM) is a powerful, built-in option for the Oracle Database that provides advanced data mining algorithms directly within the database environment. This enables data analysts and scientists to build and deploy predictive models without moving data out of the secure, high-performance Oracle Database. ODM supports a wide range of techniques, including classification, regression, clustering, association rules, and feature selection. Its primary applications include forecasting future trends, identifying new customers' selling interests through affinity analysis, and detecting sophisticated patterns indicative of fraud or anomalies, all while leveraging th...
Data scientists, business analysts, and database administrators working within an Oracle Database ecosystem who need to perform advanced predictive analytics and data mining directly on their operational data. It is particularly useful for applications in customer analytics, risk management, and fraud detection where data volume and security are paramount.
Pricing can change. Confirm current plans and terms with the vendor.
Oracle Data Mining is listed in Data Mining Software.
Oracle Data Mining is listed with Oracle as the vendor.
Buyers often compare Oracle Data Mining with other Data Mining Software tools such as SAS Enterprise Miner, IntelliFront BI, AlertMiner, limestats. Review ratings, pricing, and fit before choosing.
Yes. Use the Write a review button on this page to submit a software review for Oracle Data Mining.
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