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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
MPGN - An Approach for Discovering Class Association Rules

MPGN - An Approach for Discovering Class Association Rules

Mitov, Iliya

Paperback

General Computers

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ISBN10: 6139970040
ISBN13: 9786139970049
Publisher: Lap Lambert Academic Pub
Published: Nov 30 2018
Pages: 208
Weight: 0.69
Height: 0.48 Width: 6.00 Depth: 9.00
Language: English
Data mining is of great importance in the overall process of knowledge discovery. Different rule quality measures and rule ordering schemes can be applied in the process of rule selection. The mining of association rules is a typical data mining task that works in an unsupervised manner. A major advantage of association rules is that they are theoretically capable to reveal all interesting relationships in a database. Class-Association Rules (CAR) algorithms have a special place within the family of classification algorithms. This type of classifiers offers a number of advantages: efficiency of the training regardless of the training set; easy handling with high dimensionality; very fast classification; high accuracy; classification model easily comprehensible for humans. This work describes a new associative classifier - MPGN, that effectively builds and stores the set of rules in multilayer structures. The experiments performed proved the viability of the proposed approach, showing good performance of MPGN compared to other similar rules-based classifiers, especially in the case of large-volume data analysis with many classes and unevenly distributed.

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