Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
3 results
Search Results
Item Temporal topic modeling of scholarly publications for future trend forecasting(Springer Verlag service@springer.de, 2017) Bhopale, A.P.; Kamath S․, S.S.The volume of scholarly articles published every year has grown exponentially over the years. With these growths in both core and interdisciplinary areas of research, analyzing interesting research trends can be helpful for new researchers and organizations geared towards collaborative work. Existing approaches used unsupervised learning methods such as clustering to group articles with similar characteristics for topic discovery, with low accuracy. Efficient and fast topic discovery models and future trend forecasters can be helpful in building intelligent applications like recommender systems for scholarly articles. In this paper, a novel approach to automatically discover topics (latent factors) from a large set of text documents using association rule mining on frequent itemsets is proposed. Temporal correlation analysis is used for finding the correlation between a set of topics, for improved prediction. To predict the popularity of a topic in the near future, time series analysis based on a set of topic vectors is performed. For experimental validation of the proposed approach, a dataset composed of 17 years worth of computer science scholarly articles, published through standard IEEE conferences was used, and the proposed approach achieved meaningful results. © Springer International Publishing AG 2017.Item Novel hybrid feature selection models for unsupervised document categorization(Institute of Electrical and Electronics Engineers Inc., 2017) Bhopale, A.P.; Kamath S․, S.Dealing with high dimensional data is a challenging and computationally complex task in the data pre-processing phase of text clustering. Conventionally, union and intersection approaches have been used to combine results of different feature selection methods to optimize relevant feature space for document collection. Union method selects all features from considered sub-models, whereas, intersection method selects only common features identified by sub-models. However, in reality, any type of feature selection can cause a loss of some potentially important features. In this paper, a hybrid feature selection model called Modified Hybrid Union (MHU) is proposed, which selects features by considering the individual strengths and weaknesses of each constituent component of the model. A comparative evaluation of its performance for K-means clustering and Bio-inspired Flockbased clustering is also presented on standard data sets such as OWL-S TC and Reuters-21578. © 2017 IEEE.Item Concise semantic analysis based text categorization using modified hybrid union feature selection approach(Institute of Electrical and Electronics Engineers Inc., 2018) Bhopale, A.P.; Kamath S․, S.; Tiwari, A.Text categorization mainly comprises of deriving a representation of the corpus in a standard bag-of-words format. The merit of bag-of-word representations is that they considering every term as a feature, while the downside of this is that the computation cost increases with the number of features and the representation of relations between documents and features. Semantic analysis can help in gaining an edge through document and term correlation in a concept space. However, most semantic analysis techniques have their own limitations when used for text categorization. In this work, a Concise Semantic Analysis (CSA) technique that extracts concepts from corpus and then interpret the document & word relationship in a given concept space is proposed. To improve the performance of CSA, a novel feature selection technique called the Modified hybrid union (MHU) was designed, which considerably reduced computation time and cost. To experimentally validate the proposed approach, MHU based CSA was applied to the problem of text categorization. Experiments performed on standard data sets like Reuters-21578 and WSDL-TC, show that the proposed CSA with MHU approach significantly improved performance in terms of execution time and categorization accuracy. © 2018 IEEE.