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SAS Data Mining
There are multiple the reason to choose SAS software for data mining, some of the reasons are as follows:
- SAS has been an unshakeable leadership in data mining and predictive analytics over the year.
- SAS provides a modern industry-specific technique for data mining and analytics. SAS provides breadth and depth algorithms that are extended to industry-specific algorithms and making their solution to be best-in-className for providing solutions for a variety of business issues.
- SAS has flexible processing and deployment – not one size fits all. In this SAS can work on a simple desktop as a single machine solution to a high-performance distributed processing machine solution depending on the user requirement.
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For Data Mining in SAS, there are various products available for it. The SAS software for data mining are as follows:
1. SAS Enterprise Miner:
SAS Enterprise Miner is a descriptive and predictive modelling software that provides insights that help to drive or simulate better decision making. Using SAS Enterprise Miner there is the possibility that the user can streamline the data mining process to develop models quickly, understanding the key relationships and find the patterns that matter most from the data.
- Build better models with better tools: SAS Enterprise provides a shorten model development time for data mining and statistics output. The SAS Enterprise is an interactive, self-documenting process flow diagram environment that efficiently maps the entire data mining process to help in producing the best possible result.
- Empower business users: Through the use of the SAS Rapid Predictive Modeler in the SAS Enterprise Miner, business users and subject matter experts with very limited statistical skills can generate their own models. It is an easy-to-use GUI steps that help the users to get through the workflow of the data mining tasks. The analytics results from the data mining task are displayed in a chart that is easy to understand so that the user can provide useful insights that are needed for better decision making.
- Improve prediction accuracy, Share reliable results: Through SAS Enterprise Miner, it is possible to create the better or best possible performance models using innovative algorithms and methods that are created based on industry standards. The results can be verified by having visual assessment available and validation metrics. The user can easily compare the prediction and assessment statistics from the models that are built with different approaches by displaying them side by side. The resulting diagram output can serve or provide a self-documenting template that is updated or applied to a new problem initiated by the user without restarting.
- Automate model deployment and scoring: While using SAS Enterprise Miner, scoring code is automatically generated for all the stages in the model development. This helps in the elimination of potentially costly errors that can stem or arise from manually rewriting and converting code. There is a possibility to embed the scoring code into the business process or deploy it in the real-time or batch environment within SAS, web or directly into the relational databases. This helps in producing more accurate results and save time.
Features
- SAS Enterprise Miner provides an easy-to-use GUI and batch processing
- SAS Enterprise Miner has sophisticated data preparation, summarization and exploration
- SAS Enterprise Miner is an advanced predictive and descriptive modelling
- SAS Enterprise Miner has an open-source integration with R
- SAS Enterprise Miner is a high-performance capabilities software
- SAS Enterprise Miner is a fast, easy, self-sufficient way for business users to create and generate models
- SAS Enterprise Miner provides model comparisons, reporting and management
- SAS Enterprise Miner contains the automated scoring process
- SAS Enterprise Miner has the ability to call SAS Viya actions within a process flow
- SAS Enterprise Miner uses scalable processing to create the models
- SAS Enterprise Miner has a cloud deployment option
2. SAS Factory Miner:
In SAS Factory Miner, the work done by the enabling modelers and statisticians is more efficient. Therefore, they have more time to unearth and gain valuable insights buried in segments to reveal new opportunities, expose hidden risks and make smarter, well-timed decisions.
Features:
- Data preparation: In this step or process of SAS Factory Miner includes an interactive data preparation tools which make it easier to apply onto the required data for transformation, deriving new required variables and running smart and intelligent feature selection method, for example, variable selection based on trees and random forests.
- Customizable model templates: SAS Factory Miner provides an out-of-the-box model building templates that can be customised and shared across various projects and users.
- Self-service machine learning techniques: In SAS Factory Miner there are machine learning techniques that include regression, decision trees, random forest, neural networks, support vector machines and many more.
- Champion model identification: SAS Factory Miner has and uses a variety of interactive, customisable assessment techniques to help in automatically select the champion model for each segment.
- Model exception identification: In SAS Factory Miner, there are standardised, easy-to-understand reports that provide pinpoint issues with models used and helps in identifying the best models with a high level of confidence. Through this, the user can easily recognise the underperforming model.
- Model retraining: SAS Factory Miner allows the user to retrain models over time using new data and variables which includes the REST endpoints.
- Scalable processing: SAS Factory Miner can run analytical procedures on a single machine, via grid computing or in-memory processing.
- Flexible model deployment: SAS Factory Miner allows the user to deploy models in a database or in Hadoop to score new data using SAS Scoring Accelerator.
- Add-on to SAS® Enterprise Miner™: SAS Factory Miner runs as an add-on to SAS Enterprise Miner.
Other points around SAS Factory Miner:
- SAS Factory helps in boosting model building productivity
- SAS Factory has an automate model development.
- SAS Factory can explore new ideas faster.
- SAS Factory can create and put models into operation quickly.
3. SAS Model Manager:
SAS Model Manager and open-source models within projects or as standalone models can be select for development. After which develop and validate candidate models selected. Once the candidate models are deployed, assess and compare candidate models for champion model selection. Then publish and monitor champion and challenger models to ensure getting an optimal model performance.
Feature:
- SAS Model Manager has a centralized model repository to keep track and manage the user’s models.
- SAS Model Manager is analytical workflow management which allows the users to define and manage customised workflows.
- SAS Model Manager provides users with model validation
- In SAS Model Manager, performance monitoring and reporting of the models and workflow is present.
- SAS Model Manager provides a quick operationalise of the users’ analytical models. It allows rapid and automated techniques that let the user operationalise models within a few clicks, both in batches and in real-time.
- SAS Model Manager helps in keeping the users’ models performing at their highest levels. Performance benchmarking reports and alerts are created and generated for easy tracking of model decay indication
- SAS Model Manager ensures transparency and analytics governance of the models. This allows effective collaboration by letting the users track the progress through each step of the model management process.
- Through the SAS Model Manager, gain complete knowledge of your model collections.
4. SAS Scoring Accelerator:
SAS Scoring Accelerator helps in eliminating inconsistencies that can be caused by excessive data movement and latency from conventional processes done for scoring data. It helps in preventing the delays results from slowing the process of analytics. It also captures timely insights from the data to help you seizing opportunities had could have been missed.
Features/Benefits:
- SAS Scoring Accelerator helps in boosting model performance by providing faster insights.
- SAS Scoring Accelerator can be used to help in increasing data mining and IT productivity as it reduces the need to manually revalidate code for set models.
- SAS Scoring Accelerator can be used or provide help in streamlining analytic deployment as there is no extra need to move data between SAS and the database for scoring.
- Through SAS Scoring Accelerator there is reduce data movement and replications which ensures the integrity of the data as it crucial for data governance.
5. SAS Text Miner:
Through this SAS software, it becomes easier to analyze the text data from different sources, for instance, web, comment fields. SAS Text Miner will help in deepening the understanding of the text by discovering new information, like topics and term relationships. With this new information, a user can improve their models’ performances.
Features/Benefits:
- SAS Text Miner helps in improving model performances by providing insights from the text-based data to the models’ predictive power.
- Through the use of SAS Text Miner, it possible to add subject-matter expertise as it helps in providing easy identification relevance, helps in modifying algorithms and converting documents or materials into meaningful aggregates.
- SAS Text Miner helps in automatically know more about the text as it automates the time-consuming manual activities by using machine learning and natural language processing techniques.
- Through the use of SAS Text Miner, it will help in determining what's hot and what's not in the text as it is structured into a numeric representation to be able to summarize the document and use it as an input to help in prediction and data mining modelling techniques.
6. SAS Unified Insights MM:
This SAS software can be used for self-serving data mining, AI and machine learning capabilities for a quick solution for a complex problem. It is a flexible, centralized analytics environment that can be used for the analytics life cycle.
Features/Benefits:
- SAS Unified Insights MM is a market-leading data mining, AI & machine learning
- SAS Unified Insights MM has the option of open source integration.
- SAS Unified Insights MM is a single, cohesive analytics environment type of software.
- The software provides a self-service data preparation option to the user.
- The software provides visual data exploration & insights development on the data.
- SAS Unified Insights MM helps in minimizing data movement.
- The software helps in streamlined model deployment.
- There are flexible deployment options available with SAS Unified Insights MM.
- SAS Unified Insights MM provides or has a seamless collaboration across teams.
- SAS Unified Insights MM helps in establishing effective governance across data access, analytics, reporting and model deployment based on the user requirement.
- SAS Unified Insights MM provides help in freeing up resources with scalable, automated and repeatable processes.
7. SAS Visual Statistics:
SAS Visual Statistics is a visual interactive data exploration and discovery that is combined with the ability to easily build and adjust huge numbers of predictive models on demand.
Features/Benefits:
- SAS Visual Statistics helps in providing a descriptive and predictive modelling
- SAS Visual Statistics provides an open, code-based model development option for the user to work with.
- SAS Visual Statistics is able to handle dynamic group-by processing.
- Through SAS Visual Statistics, it is possible to have a model comparison, assessment and scoring that can use existing data or new data.
- SAS Visual Statistics has a distributed, in-memory analytical processing abilities that can be used for a range of business questions.
- SAS Visual Statistics provides a flexible deployment option to the user.
- SAS Visual Statistics helps in uncover opportunities faster than your competitors as it is possible to act on observations based of the analysis output created by the modelling techniques.
- Through SAS Visual Statistics is possible to put better models into action faster to be able to meet the target goal or segment by running numerous scenarios simultaneously.
- SAS Visual Statistics helps in boosting analytical productivity by instantly changing or adapting the model’s predictive power based on the changes done by the user.
- Through SAS Visual Statistics gain the freedom to experiment as there is the flexibility to work on the programming language that the user prefers.
