Topic Modeling Technique for Text Mining Over Biomedical Text Corpora Through Hybrid Inverse Documents Frequency and Fuzzy K-Means Clustering

• Published in: IEEE access Vol. 7; pp. 146070 - 146080
• Main Authors: Rashid, Junaid, Adnan Shah, Syed Muhammad, Irtaza, Aun, Mahmood, Toqeer, Nisar, Muhammad Wasif, Shafiq, Muhammad, Gardezi, Akber
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; bag-of-words; Biological system modeling; biomedical; Biomedical data; Biomedical imaging; Classification; Cluster analysis; Clustering; Computational modeling; Data mining; Data processing; Discriminant analysis; Experimentation; Information retrieval; LDA; LSA; Machine learning; Modelling; Performance evaluation; Principal components analysis; Probabilistic logic; Redundancy; Task analysis; Text mining; Topic modeling; Vector quantization; Weighting
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2944973

Text data plays an imperative role in the biomedical domain. As patient's data comprises of a huge amount of text documents in a non-standardized format. In order to obtain the relevant data, the text documents pose a lot of challenging issues for data processing. Topic modeling is one of the popular techniques for information retrieval based on themes from the biomedical documents. In topic modeling discovering the precise topics from the biomedical documents is a challenging task. Furthermore, in biomedical text documents, the redundancy puts a negative impact on the quality of text mining as well. Therefore, the rapid growth of unstructured documents entails machine learning techniques for topic modeling capable of discovering precise topics. In this paper, we proposed a topic modeling technique for text mining through hybrid inverse document frequency and machine learning fuzzy k-means clustering algorithm. The proposed technique ameliorates the redundancy issue and discovers precise topics from the biomedical text documents. The proposed technique generates local and global term frequencies through the bag-of-words (BOW) model. The global term weighting is calculated through the proposed hybrid inverse documents frequency and Local term weighting is computed with term frequency. The robust principal component analysis is used to remove the negative impact of higher dimensionality on the global term weights. Afterward, the classification and clustering for text mining are performed with a probability of topics in the documents. The classification is performed through discriminant analysis classifier whereas the clustering is done through the k-means clustering. The performance of clustering is evaluated with Calinsiki-Har-abasz (CH) index internal validation method. The proposed toping modeling technique is evaluated on six standard datasets namely Ohsumed, MuchMore Springer Corpus, GENIA corpus, Bioxtext, tweets and WSJ redundant corpus for experimentation. The proposed topic modeling technique exhibits high performance on classification and clustering in text mining compared to baseline topic models like FLSA, LDA, and LSA. Moreover, the execution time of the proposed topic modeling technique remains stable for different numbers of topics.