Envisioner is Neurosoft’s next generation Data Mining software. The software uses ADO technology and can be deployed in a variety of ways; either as stand-alone mining system or embedded in other vertical applications. Envisioner is fully scalable and has the power to provide a new understanding of data by rapidly seeking, analyzing and understanding the patterns in a data set. It has the ability to solve knowledge based issues at any level of an enterprise.

Envisioner includes extensive data transformation, mining and visualization features; enabling users to design and customize the data manipulation, the mining and the reporting processes; without compromising on scalability or performance.

Envisioner can work with all types of data. This data can be raw and unstructured with inconsistent or missing values, or structured integrated data from a Data Warehouse or Data Mart and everything in between.

Moreover, Envisioner benefits from a simple, intuitive GUI (graphical user interface) that is very easy to learn. Analysis results are displayed in the form of an interactive decision tree and the defined rules are stored in the database. In just a few minutes, it examines all the relationships between the fields of data, eliminating trial-and-error guesswork, by searching and finding strong statistical relationships/relevancies. All fields and combinations of issues are ranked in order of importance. Envisioner provides both the feature rich environment required by the experienced data mining practitioner, as well as, enables highly focused mining and reporting systems to be provided for users who are new to data mining.

Finally, at the heart of what makes Envisioner such a powerful and easy-to-use tool; is its Decision Tree Induction process, which in simplified terms, acts as an automated rule generator. The Inductive Decision Tree methods have all the required mathematical power to behave as classification and prediction models.

Advantages of Envisioner

• It is scalable and therefore able to handle very large data sets. Since the size of a database is unlimited, this characteristic enables our miner to extract knowledge from any database irrespective of its size.

• There is no limit in the source database that it can perform data mining methods. Because of the use of ADO (ActiveX Data Objects), the user can open and mine every known source of data.

• It is quite fast. Because it utilizes several optimization techniques and the tool avoids a lot of unnecessary operations and computations.

• Intelligent Handling of Missing Values. Unlike many methodologies that take into consideration “missing value”, in structured or random environments; with the advent of more powerful computing technologies, the “miner” is able to be applied to very large databases with numerous variables and seek out theses values.

• It enables non-technical users to build knowledge models. The tool does not require the user to be an expert in the area of data mining. Therefore, it does not require the specification of complex parameters, options and operations, in order to work.

• It is user-friendly. All the operations are defined visually and most of the time the user only needs to utilize the mouse.

• It produces simple and easy to understand knowledge models. The results are also stored in the mined database in table formats, so the user can review them anytime.

Applications

Envisioner technology can be applied on a wide range of business uses.

Customer relationship management

Customer profiling is by far the most common application being pursued. For example: attrition analysis is a related application in which the characteristics of customers, who have already left a service, are used to predict which customers may decide to leave in the near future.

Marketing

Targeted marketing is of great interest. This includes applications such as improving response rates to direct-mail campaigns, choosing which products to market to which customers, or identifying market segments on which to concentrate promotion efforts. Market basket analysis, which shows the relationships among items purchased at the same time, is also high on the agenda.

Fraud detection

Many obvious potential frauds (such as checks being cashed by dead people) can be found without data mining, but more subtle patterns of abuse may be hard to find. This is also a good example of the fact that data mining generates a hypothesis (such as "this case may be a fraud") that human investigation must confirm.

Credit scoring

Two examples of this data mining application are developing models that predict: who is likely to be a good credit risk or who, among existing customers, is likely to become delinquent in payments.

Process control and quality control

Manufacturing constitutes another application area. Here, the data describes physical conditions (such as temperature or mechanical specs) rather than financial or demographic attributes.

Other applications

There are a host of other applications such as: using data mining for tracking the effectiveness of medical procedures or experimental drugs, for identifying success factors for use in the recruitment of employees or attracting college students, as well as,  fine-tuning food-service menus.