Boolean logic algebra driven similarity measure for text based applications

• Published in: PeerJ. Computer science Vol. 7; p. e641
• Main Authors: Abdalla, Hassan I., Amer, Ali A.
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ, Inc 29-07-2021; PeerJ Inc
• Subjects: Algebra; Algorithms; Artificial Intelligence; Boolean; Boolean algebra; Classification; Cluster analysis; Clustering; Data mining; Data Mining and Machine Learning; Datasets; Efficiency; Empirical study; Information retrieval; Machine learning; Natural Language and Speech; Similarity; Similarity measure; Similarity measures; Text categorization; Text classification; Text clustering; Vector quantization
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.641

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks.