Deep learning-based sequential pattern mining for progressive database
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METHODOLOGIES AND APPLICATION
Deep learning-based sequential pattern mining for progressive database Aatif Jamshed1 • Bhawna Mallick2 • Pramod Kumar3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Sequential pattern mining (SPM) is one of the main application areas in the field of online business, e-commerce, bioinformatics, etc. The traditional approaches in SPM are unable to accurately mine the huge volume of data. Therefore, the proposed work employs a sequential mining model based on deep learning to minimize complexity in handling huge data. Application areas such as online retailing, finance, and e-commerce face a dynamic change in data, which results in non-stationary data. Therefore, our proposed work uses discrete wavelet analysis to convert non-stationary data into time series. In the proposed SPM, a reformed hybrid combination of convolutional neural network (CNN) with long short-term memory (LSTM) is designed to find out customer behavior and purchasing patterns in terms of time. CNN is used to find the concerned itemsets (frequent) at the end of the pattern and LSTM for finding the time interval among each pair of successive itemsets. The proposed work mines the sequential pattern from a progressive database that removes the obsolete data. Finally, the accuracy of the proposed work is compared with some traditional algorithms to demonstrate its robustness. Keywords Sequential pattern mining Wavelet analysis CNN LSTM Progressive database
1 Introduction With the increased usage of Internet and database technologies, there is a rise in huge volume of data which is beyond the capacity of manual processing. The data stored in the database consist of hidden information, which can be utilized in decision making purpose for various applications such as healthcare, fraud detection, bioinformatics customer segmentation, stock market, medicinal field, and security applications (Agarwal 2013; Liu et al. 2016).
Communicated by V. Loia. & Aatif Jamshed [email protected] 1
Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand, India
2
Maverick Quality Advisory services Pvt, Ltd, GhaziabadUttar Pradesh, India
3
Department of Computer Science and Engineering, Krishna Engineering College, GhaziabadUttar Pradesh, India
Data mining is an automatic process which extracts the necessary information from a massive amount of raw data. In the business domain, data mining is used to study customer satisfaction, profits of a firm, preference of a customer, etc. Online retailers utilize the knowledge of customer behavior in order to extract information regarding the business (Almasoud et al. 2015; Zhang 2011; Lu et al. 2017; Singh and Chauhan 2009). The process of data mining varies based on the type of database employed. The different kinds of the database include relational, transaction, object-oriented, deductive, spatial, temporal, multimedia, progressive databases, etc. (Chen et al. 19
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