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Using cloud effectively in concept based text mining using grey wolf self organizing feature map

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Abstract

Cloud computing is considered to be an integral aspect in all business and this is expected to change the information technology (IT) landscape. This has been based on the model that delivers services on the internet using the pay-as-you go model that has several advantages like the no up-front cost, a lower IT staff, and a lower operation cost. A technology that is made use of for retrieval of data from huge database is known as text mining. This is used by cloud for efficiently retrieving data from the data centres of cloud. In providing navigation as well as mechanisms for browsing intuitively, text document clustering has an important role. This is done by organizing huge amounts of information into smaller number of clusters. Bag of words (BoW) is a representation that is used for the clustering of these methods but in many case it is not satisfactory as relations that exist between terms that don’t co-occur are ignored. To handle this problem a document level and sentence level integration of the concepts is made. This increases the space of the feature vector and also brings down the clustering algorithm’s efficiency. In order to overcome this a self-organizing feature map (SOFM) based algorithm makes use of the concepts of genetic algorithm (GA) along with grey wolf optimization (GWO) which are considered popular in the SOFM. The goal of the SOFM-GA is to find an optimal topology of network (the number of neurons and their array dimension) along with an optimal training parameter like the scheduling of learning rate and the annealing of neighborhood width. The SOFM-GWO and the GWO-based approach to the formation of SOFM are compared with the SOM standard relating to quality and the weights and map generated. The results of the experiment show that this method achieved better results.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama University, Chennai, Tamilnadu, India

    R. Thilagavathy

  2. Department of Information Technology, Jeppiaar Engineering College, Chennai, Tamilnadu, India

    R. Sabitha

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  1. R. Thilagavathy
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  2. R. Sabitha
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Correspondence to R. Thilagavathy.

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Thilagavathy, R., Sabitha, R. Using cloud effectively in concept based text mining using grey wolf self organizing feature map. Cluster Comput 22 (Suppl 5), 10697–10707 (2019). https://doi.org/10.1007/s10586-017-1159-y

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  • DOI: https://doi.org/10.1007/s10586-017-1159-y

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