Intelligent Data Analysis: Developing New Methodologies by Hsiao-Fan Wang

By Hsiao-Fan Wang

Trend acceptance has a protracted historical past of purposes to info research in enterprise, army and social fiscal actions. whereas the purpose of trend reputation is to find the trend of a knowledge set, the dimensions of the information set is heavily with regards to the method one adopts for analysis.

Intelligent information research: constructing New Methodologies via development Discovery and restoration tackles these info units and covers quite a few matters on the subject of clever info research in order that styles from common or infrequent occasions in spatial or temporal areas will be published. This e-book brings jointly present learn, effects, difficulties, and functions from either theoretical and useful ways.

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Additional resources for Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery

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Where current systems require manual intervention by data analysis experts if a data analysis module needs updating, our architecture automatically detects changes in performance or requirements, builds a new data analysis model, and wraps the model into an executable Java library with standard API that can easily be accessed by operational systems. When building data analysis models, the wizard takes into account high-level user requirements regarding an explanation facility, simplicity of an explanation, adaptability, accuracy, and so on.

The lower Automatic Intelligent Data Analysis level is the level of details like the potentially infinite number of possible values in a continuous numeric domain of discourse. At the higher level of abstraction we only deal with a finite number of symbols by abstracting from details. In other words, the granularity of information can be either fine or coarse (potentially, entire hierarchies of granularity are conceivable). ” What this approach allows us to do is to represent inherently symbolic information like the type of analysis problem mentioned above at the same level as symbols like “high accuracy” which in fact are abstractions from a level of finer granularity.

CAse studIes The SPIDA platform discussed in this chapter is a research demonstrator, and its main purpose was to build and test methods for automatically generating predictive models in a data analysis process. Although the platform is constantly used in our team for doing day-to-day data analysis applying it to operational processes requires further work. In this section we briefly mention three successful internal applications that build on SPIDA technology, that is, methods for automatically building models from data.

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