Data mining-based information extraction research of knowledge software

2018 ◽  
pp. 243-245
Keyword(s):  
Data Mining ◽  
Information Extraction

scholarly journals Information Extraction for Clinical Data Mining: A Mammography Case Study

2009 ◽  
Author(s):  
Houssam Nassif ◽  
Ryan Woods ◽  
Elizabeth Burnside ◽  
Mehmet Ayvaci ◽  
Jude Shavlik ◽  
...  
Keyword(s):  
Data Mining ◽  
Information Extraction ◽  
Clinical Data ◽  
Clinical Data Mining

Rural Geo-Information Extraction Based on GNSS Data Mining

2013 ◽  
Author(s):  
Yaping Cai ◽  
Caicong Wu ◽  
Jing Zhao
Keyword(s):  
Data Mining ◽  
Information Extraction ◽  
Gnss Data

scholarly journals Biologically Inspired Techniques for Data Mining

2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

Study of Potential Classification of Lost Students in College Based on Information Extraction on Text-Based Social Media; Case Study of Panca Budi Pembangunan University

2021 ◽  
Vol 8 (11) ◽  
pp. 325-331
Author(s):  
Eko Hariyanto ◽  
Sri Wahyuni ◽  
Supina Batubara
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Text Mining ◽  
Information Extraction ◽  
Preventive Measure ◽  
Drop Out ◽  
Training Data ◽  
Computational Algorithms ◽  
Technological Readiness

The main problem studied in this study is the large number of lost students who harm universities because of the difficulty of monitoring or monitoring as a preventive measure. Therefore, this research becomes very important to be done so that college institutions can make efforts to detect early (classification) of students who potentially cannot complete their studies on time or students who will drop out (DO). Thus, PT institutions through related parties such as academic guidance lecturers, academic bureaus and others can do initial prevention by providing the best solution or solution to the problems faced by students. This research aims to determine the training data model consisting of academic and non-academic factors (including the results of extracting information from social media). Furthermore, this model is used as a basis for classifying students who have the potential to "graduate on time", "graduate not on time", and "DO". The method approach used is quantitative with text mining computational algorithms for the process of extracting knowledge / information from social media which is further used in data training, as well as data mining computational algorithms for the process of classification of potential completion of student studies. The mandatory external targeted in the first year is the publication of the international journal Scopus Q4 and in the second year is the publication of the international journal Scopus Q3. For additional external targets in the first and second years respectively are the publication of international journals indexed on reputable indexers, ISBN teaching books and copyrights. The level of technological readiness (TKT) in this study up to level 2 is the formulation of technological concepts and applications to classify the potential completion of student studies using data mining. Keywords: [student lost, knowledge/information extraction, data classification, text mining, data mining].

Biologically Inspired Techniques for Data Mining

2016 ◽  
pp. 2275-2284
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

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