Knowledge Acquisition Approach Based on Incremental Objects From Data With Missing Values

Abstract

Knowledge acquisition is the process of extracting useful knowledge from data sets to analyze data in areas of data mining and knowledge discovery. Most current knowledge acquisition work mainly focuses on static data. However, due to the dynamic characteristics of data, the objects grow at an unprecedented rate in real-world data sets. The incremental objects with a dynamic environment significantly affect knowledge updating. To maintain the effectiveness of knowledge from the dynamic data, it is necessary to update the knowledge timely. So far, there are relatively few studies on knowledge acquisition for the data with missing feature values, i.e., incomplete data. To handle with this issue, an incremental updating manner of the accuracy matrix and coverage matrix are first proposed on the basis of the computations of the tolerance classes in incomplete data, which plays an important role in the knowledge acquisition process. Then, an incremental knowledge acquisition algorithm is proposed when some new objects added to the data with missing values. Finally, some numerical experiments are conducted to evaluate the efficiency of the proposed algorithm.

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“…In terms of knowledge extraction, there is an increasing demand for transforming raw data into knowledge, which is of great significance for decision making, optimization, and analysis [149][150][151]. Tang et al [152] have proposed a method to acquire knowledge from documents, which can process documents with high complexity.…”