Pdf Granular Computing As A Basis For Consistent Classification Problems

Pdf Granular Computing As A Basis For Consistent Classification Problems In the following sections, we will demonstrate the appli cation of the granular computing model for the study of a specific data mining problem known as the consistent clas sification problems. Pdf | within a granular computing model of data mining, we reformulate the consistent classification problems.

Pdf Granular Computing Based Approach For Classification Towards A modified prism classification algorithm is presented based on the framework of granular computing, which reasonably sacrifice the accuracy of description in a controlled level in order to improve the comprehensibility and predictability of classification rules. Within a granular computing model of data mining, we reformulate the consistent classification problems. the granulation structures are partitions of a universe. Basic ingredients of granular computing are subsets, classes, and clusters of a universe. Abstract—granular computing arose as a synthesis of insights into human centred information processing by zadeh in the late'90s and the granular computing name was coined, at this early stage, by ty lin.

Pdf Granular Computing Using Information Tables Basic ingredients of granular computing are subsets, classes, and clusters of a universe. Abstract—granular computing arose as a synthesis of insights into human centred information processing by zadeh in the late'90s and the granular computing name was coined, at this early stage, by ty lin. As fundamental abstract constructs supporting the human centered way of granular computing (grc), information granules can be used to distinguish different classes of data from the perspective of easily understood geometrical structure. We present a granular computing view of data mining in particular to classification problems. with the induce of partition and cover ing defined by a set of attribute values, one can find a solution by granulation. By classification in the granular computing framework, we can study formally and systematically. we modify the existing prism algorithm the classification rules. The proposed granular computing based classification method provides an approach for classifying algebraic structure based granularity, and combines granular computing theory and classification theory of machine learning.