Interval Type-2 Intuitionistic Fuzzy Logic System for Forecasting the Electricity Load

Eyoh, Imo; Eyoh, Jeremiah; Umoeka, Ini

doi:10.31695/ijasre.2020.33903

Cited by 1 publication

(1 citation statement)

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“…The proposed clustering algorithm is utilized to cluster the documents based on the topics. The proposed clustering algorithm is an extension of Fuzzy C Means Clustering (FCM) and handles more uncertainty in data than FCM [23]. In the proposed methodology, two different fuzzifiers are utilized which are defined as a fuzzy degree.…”

Section: Type-2 Intuitionistic Fuzzy C-means Clustering For Document Clusteringmentioning

confidence: 99%

Type2 IFC with SOA for Topic Detection and Document Clustering Analysis

Perumal¹,

Mathivanan²

2021

Preprint

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The automatic document clustering and topic extraction from the corpus provides a very essential requirement in many real time applications. The document clustering and topic detection is utilized to locating data quickly. Hence, in this paper, Type 2 Intuitionistic Fuzzy Clustering and Seagull Optimization Algorithm (Type 2 IFCSOA) is developed for document clustering and topic detection. The Type 2 IFCSOA is utilized to cluster the documents. Additionally, ensemble approach is utilized to identify by the topics from the clustered documents. In the proposed methodology, the pre-processing is utilized to remove unwanted information from the documents such as tokenization, stop word removal and stemming process. After that, the proposed method is utilized to cluster the documents. The clustered documents are labeled with the basis of clusters. After that, to achieve topic detection, the ensemble approach is utilized with feature extraction phases such as Term Frequency- Inverse Document Frequency (TF-IDF), Mutual information (MI), Text Rank Algorithm and analysis of keyword taking out from co-occurrence statistical -Information (CSI). The proposed methodology is implemented in MATLAB and performances were evaluated with the statistical measurements such as precision, recall, accuracy, sensitivity, purity measure and entropy. The proposed method is compared with the conventional methods such as Fuzzy C Means clustering (FCM), FCM-Particle Swarm Optimization (PSO), FCM-Genetic Algorithm (GA) and K means clustering.

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Section: Type-2 Intuitionistic Fuzzy C-means Clustering For Document Clusteringmentioning

confidence: 99%