How A PLM Data Mining Approach Works

How A PLM Data Mining Approach Works

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In the scientific fields, it is necessary to appreciate the importance of data mining.  This, of course, is known as just one interdisciplinary sub-field within the computer science articulation.  Data mining involves the process of identifying patterns that emerge from within larger sets of information to incorporate strategies where artificial intelligence, machine learning, database systems, and statistics meet.

In all, the data mining objective of Quantisweb simulation software is to extrapolate accurate information from any set of data and then to break down specific pieces of information into a broader understanding of the infrastructure, so you can use it in the future.  Aside from raw analysis, data mining utilizes:

  • database and data management prospects
  • the pre-processing of data
  • interference and model sensitivities
  • visualization
  • measuring how complex or interesting a piece of data is
  • online updating
  • post-processing of discovered structures

There are six common task classes in the data mining process.

  • Anomaly Detection

Also called outlier detection and deviation detection, this task is characterized by the finding of unusual data or errors that may require further investigation

  • Association Rule Learning

Also called dependency modeling or market basket analysis, this task seeks out relationships between at least two variables. For example, a supermarket collects information on the buying habits of a particular customer in order to determine which products or which departments the frequent

  • Clustering

This task looks for groups and structures within the data that might be similar, in some way, to other related—but outside—data

  • Classification

This task determines the general structure so it can be applied to newly discovered data. For example, an e-mail program that can attempt to determine if an incoming message is spam or legitimate (so it knows which folder to send it to).

  • Regression

This task serves to analyze which attempts for finding a new function within the system will, in fact, model the data but only exposing those with both the least number of attempts and the least likelihood of errors.

  • Summarization

The final task is to take a step back and look more compactly at the data set. This can include visualization and report generation, which is used, then, to better understand the processes conclusions as well as for applications to future processes.