Abstract
In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of dataset and attributes. This knowledge is applied to the concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textual documents, text mining is applied. The text mining is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM) is defined for effective text clustering. This VMM involves the learning of conceptual terms; these terms are grouped in Significant Term List (STL). VMM model is appropriate combination of layer 1 arch with Analysis of Bilateral Intelligence (ABI). The frequent update of conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorithm is used for learning texual pattern in the Virtual Mining Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning algorithm.
Highlights
Engineering problems are classified into related to the problem definition of a particular field of two kinds, static and dynamic
Whereas the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model
The text mining is the sub-domain in data mining
Summary
Engineering problems are classified into related to the problem definition of a particular field of two kinds, static and dynamic. The implementation model derives some problems are comparatively less complex because the sort of knowledge of the real time decision making behaviour of the problem is static till the end of its life behaviour such as artificial intelligence and swarm time, whereas the dynamic natured problems are intelligence. It has a complex set of rules compared with the changing its parameters and attributes more frequently mathematical model. The static natured problem requires static rules which will solve problems in the most of the comparatively less effort than the dynamic one The combination of k-means with spectral analysis (Zha et al, 2001), extended k-means algorithm (Kotsiantis and Pintelas, 2004), the Spatial Mining (Ng and Han, 1994) Principal Component Analysis (Jain et al, 1999) is a concept which based on the well-known image processing technique, the Locality Preserving Index (LPI) (Agrafiotis and Xu, 2002; Cai et al, 2005; 2011), Divide-and-merge (Cheng et al, 2006) conceptual model is proposed (Lebanon, 2006) in the literature which has performance limitations due to more epochs and repeated iterations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
