Tools are available for data mining


 2.     What tools are available for data mining?



 

Data mining is looking for hidden, valid, and all the possible useful patterns in large size data sets. Data Mining is a technique which helps you to discover unsuspected/undiscovered relationships amongst the data for business gains.


 

SAS Data mining:

 

Statistical Analysis System is a product of SAS. It was developed for analytics and data management. It is one of the best data mining programs which offers a graphical UI for non technical users.

 

Features:

 

SAS Data mining tools help you to analyze Big data

It is an ideal tool for Data mining, text mining & optimization.

SAS offers distributed memory processing architecture which is highly scalable

 

Teradata:

 

Teradata is a massively parallel open processing system for developing large-scale data warehousing applications. Teradata can run on Unix/Linux/Windows server platform.

 

Features:

 

Teradata Optimizer can handle up to 64 joins in a query.

Tera data has a low total cost of ownership. It is easy to set up, maintain, and administrate.

It supports SQL to interact with the data stored in tables. It provides its extension.

It helps you to distribute the data to the disks automatically with no manual intervention.

Teradata provides load & unload utilities to move data into/from Teradata System.

 

Board:

 

Board is a Management Intelligence Toolkit. It combines features of business intelligence and corporate performance management. It is designed to deliver business intelligence and business analytics in a single package.

 

Features:

 

Allows you to Analyze, simulate, plan and predict using a single platform

To build customized analytical and planning applications.

Board All-In-One combines BI, Corporate Performance Management, and Business Analytics.

It empowers businesses to develop and maintain sophisticated analytical and planning applications.

The proprietary platform helps to report by accessing multiple data sources.

 

Inetsoft:

 

Inetsoft's Data mining tool style Intelligence is useful data mining and intelligence platform. It allows the quick and flexible transformation of data from various sources.

 

Features:

 

It helps you to access structured and semi-structured sources, on-premise applications

Allows you to optimize apps for data consumption and updating

Offer customized and secure levels of data exploration and reporting.

Scale up for large data sets of users using Inbuilt Spark platform

Generate paginated reports with embedded business logic and parameterization.

.

.

.


 

https://www.guru99.com/best-data-mining-tools.html

Comments

Post a Comment

Popular posts from this blog

Limitations of traditional data analysis

Image
 7 .      What are the limitations of traditional data analysis? Traditional statistics is about analysing and summarizing data, and suited to processing lesser amounts of linear, repeatable data to find a solution. It’s very useful in environments where data, and the relationships between the data, are relatively stable. It is still by far the most commonly used methodology and continues to be good for mid to long term forecasting based on prior sales history, where a few hundred or even fewer data points may generate a reasonable forecast. The important limitations of statistics are:   Statistics laws are true on average. Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only. Statistical methods are best applicable to quantitative data. Statistics cannot be applied to heterogeneous data. If sufficient care is not exercised in collecting, analysing and interpreting the data, statistical results might be misleadi
Read more

Traditional data analysis

Image
 6 .      What is Traditional data analysis (descriptive and inferential)?          Traditional data is the structured data which is being majorly maintained by all types of businesses starting from very small to big organizations. In traditional database system a centralized database architecture used to store and maintain the data in a fixed format or fields in a file. For managing and accessing the data Structured Query Language (SQL) is used. 3 Characteristic of Traditional Data: ·        Traditional analytics is static.   Traditional data analytics typically relies on dashboards composed of visualizations. These dashboards are based on common business questions and are predefined well in advance.   ·        Traditional analytics answers “what.”   Traditional analytics answers a series of “what” questions and leaves the user to determine “why” through their own analyses.   ·        Traditional analytics is driven by hypotheses.   Dashboards are typically predefined based on common
Read more

Technical requirements of big data

Image
2.       What are the technical requirements of big data? This data boom presents a massive opportunity to find new efficiencies, detect previously unseen patterns and increase levels of service to citizens, but Big Data analytics can’t exist in a vacuum. Because of the enormous quantities of data involved in these solutions, they must incorporate a robust infrastructure for storage, processing and networking, in addition to analytics software. 1.       Storage Solutions Often, organizations already possess enough storage in-house to support a Big Data initiative. (After all, the data that will be processed and analyzed via a Big Data solution is already living somewhere.) However, agencies may decide to invest in storage solutions that are optimized for Big Data. While not necessary for all Big Data deployments, flash storage is especially attractive due to its performance advantages and high availability.   2. Processing Power   Servers intended for Big Data analytics must have enoug
Read more