An Improved Similarity Measure for Text Clustering and Classification

2015 ◽  
Vol 21 (11) ◽  
pp. 3583-3590 ◽  
Author(s):  
G Suresh Reddy ◽  
T. V Rajini Kanth ◽  
A Ananda Rao
Keyword(s):  
Similarity Measure ◽  
Text Clustering ◽  
Clustering And Classification

scholarly journals A set theory based similarity measure for text clustering and classification

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ali A. Amer ◽  
Hassan I. Abdalla
Keyword(s):  
Set Theory ◽  
Similarity Measure ◽  
Similarity Measures ◽  
Text Clustering ◽  
Plagiarism Detection ◽  
K Nearest Neighbor ◽  
Single Measure ◽  
Highly Effective ◽  
Clustering And Classification ◽  
Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

scholarly journals Software Component Clustering and Classification Using Novel Similarity Measure

2015 ◽  
Vol 19 ◽  
pp. 866-873 ◽  
Author(s):  
Chintakindi Srinivas ◽  
Vangipuram Radhakrishna ◽  
C.V. Guru Rao
Keyword(s):  
Similarity Measure ◽  
Software Component ◽  
Clustering And Classification

scholarly journals Real Time Symptomatic Analysis for Efficient Disease Prediction and Recommendation Generation Using Multi Level Symptom Similarity Measure

2018 ◽  
Vol 7 (4.5) ◽  
pp. 40
Author(s):  
Sathish Kumar.P.J ◽  
Dr R.Jagadeesh Kan
Keyword(s):  
Similarity Measure ◽  
Disease Prediction ◽  
Data Set ◽  
Second Stage ◽  
Class Level ◽  
Clustering And Classification ◽  
Multi Level ◽  
Noticeable Rate ◽  
High Dimensional Clustering

The problem of high dimensional clustering and classification has been well studied in previous articles. Also, the recommendation generation towards the treatment based on input symptoms has been considered in this research part. Number of approaches has been discussed earlier in literature towards disease prediction and recommendation generation. Still, the efficient of such recommendation systems are not up to noticeable rate. To improve the performance, an efficient multi level symptom similarity based disease prediction and recommendation generation has been presented. The method reads the input data set, performs preprocessing to remove the noisy records. In the second stage, the method performs Class Level Feature Similarity Clustering. The classification of input symptom set has been performed using MLSS (Multi Level Symptom Similarity) measure estimated between different class of samples. According to the selected class, the method selects higher frequent medicine set as recommendation using drug success rate and frequency measures. The proposed method improves the performance of clustering, disease prediction with higher efficient medicine recommendation.  

Text Clustering Using a Suffix Tree Similarity Measure

2011 ◽  
Vol 6 (10) ◽  
Author(s):  
Chenghui HUANG ◽  
Jian YIN ◽  
Fang HOU
Keyword(s):  
Similarity Measure ◽  
Suffix Tree ◽  
Text Clustering ◽  
Tree Similarity

scholarly journals Boolean logic algebra driven similarity measure for text based applications

2021 ◽  
Vol 7 ◽  
pp. e641
Author(s):  
Hassan I. Abdalla ◽  
Ali A. Amer
Keyword(s):  
Similarity Measure ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Similarity Measures ◽  
Boolean Logic ◽  
K Nearest Neighbor ◽  
Complex Design ◽  
Clustering And Classification

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.

scholarly journals TEXT CLUSTERING IN CONCEPT BASED MINING

2016 ◽  
pp. 32-34
Author(s):  
PRADNYA S. RANDIVE ◽  
NITIN N. PISE
Keyword(s):  
Statistical Analysis ◽  
Text Mining ◽  
Similarity Measure ◽  
Text Clustering ◽  
Concept Analysis ◽  
Experimental Result ◽  
Combined Approach ◽  
Clustering Quality ◽  
Concept Based Mining ◽  
Mining Model

In text mining most techniques depends on statistical analysis of terms. Statistical analysis trances important terms within document only. However this concept based mining model analyses terms in sentence, document and corpus level. This mining model consist of sentence based concept analysis, document based and corpus based concept analysis and concept based similarity measure. Experimental result enhances text clustering quality by using sentence, document, corpus and combined approach of concept analysis.

Sign in / Sign up

Export Citation Format

Share Document