A New Biomedical Text Summarization Method Based on Sentence Clustering and Frequent Itemsets Mining

2019 ◽  
pp. 144-152
Author(s):  
Oussama Rouane ◽  
Hacene Belhadef ◽  
Mustapha Bouakkaz
Keyword(s):  
Frequent Itemsets ◽  
Text Summarization ◽  
Biomedical Text ◽  
Summarization Method ◽  
Frequent Itemsets Mining ◽  
Sentence Clustering

Combine clustering and frequent itemsets mining to enhance biomedical text summarization

2019 ◽  
Vol 135 ◽  
pp. 362-373 ◽  
Author(s):  
Oussama Rouane ◽  
Hacene Belhadef ◽  
Mustapha Bouakkaz
Keyword(s):  
Frequent Itemsets ◽  
Text Summarization ◽  
Biomedical Text ◽  
Frequent Itemsets Mining

scholarly journals Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

2018 ◽  
Vol 84 ◽  
pp. 42-58 ◽  
Author(s):  
Mozhgan Nasr Azadani ◽  
Nasser Ghadiri ◽  
Ensieh Davoodijam
Keyword(s):  
Text Summarization ◽  
Biomedical Text ◽  
Itemset Mining ◽  
Clustering Approach ◽  
Sentence Clustering

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

Frequent Itemsets Mining of SCADA Data Based on FP-Growth Algorithm

2020 ◽  
Author(s):  
Xiaolei Ma ◽  
Yongguang Li ◽  
Ran Liu ◽  
Yanjun Zhang ◽  
Liya Ma ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Frequent Itemsets Mining

scholarly journals Application of Frequent Itemsets Mining to Analyze Patterns of One-Stop Visits in Taiwan

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e14824 ◽  
Author(s):  
Chun-Yi Tu ◽  
Tzeng-Ji Chen ◽  
Li-Fang Chou
Keyword(s):  
Frequent Itemsets ◽  
One Stop ◽  
Frequent Itemsets Mining

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

Using hashing and lexicographic order for Frequent Itemsets Mining on data streams

2019 ◽  
Vol 125 ◽  
pp. 58-71 ◽  
Author(s):  
Lázaro Bustio-Martínez ◽  
Martín Letras-Luna ◽  
René Cumplido ◽  
Raudel Hernández-León ◽  
Claudia Feregrino-Uribe ◽  
...  
Keyword(s):  
Data Streams ◽  
Lexicographic Order ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining
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