Deep learning-based sequential pattern mining for progressive database

Jamshed, Aatif; Mallick, Bhawna; Kumar, Pramod

doi:10.1007/s00500-020-05015-2

Cited by 31 publications

(10 citation statements)

References 35 publications

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“…Furthermore, FCSPs will be used instead of FSPs to recommend more suitable learning resources to learners. Other potential interesting future work includes feature selection in actionable SPs [ 22 ], visualization of FCSPs [ 3 , 13 ], and mining FCSP with a deep neural network [ 17 , 20 , 28 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Mining sequential patterns with flexible constraints from MOOC data

Song

Fournier-Viger

2022

Appl Intell

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Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.

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Section: Discussionmentioning

confidence: 99%

Mining sequential patterns with flexible constraints from MOOC data

Song

Fournier-Viger

2022

Appl Intell

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“…Smart City projects are projected to expand in the future, revolutionizing areas such as health care, healthcare, and policing, while also promoting the encrypting development [24] and progress of engaged people who will adopt and use technological technologies and services like Smart People. Future study can have utilized different deep learning frameworks for progressive databases [25]. This study will continue using factors such as: power efficiency, service quality, data transmission speed, range, sensor size, data storage, data transmission reliability, delivery cost, network type and processor to review these published papers on sensor and routing protocols.…”

Section: Future Workmentioning

confidence: 99%

An Analysis of Trends, Developments and Transparent Issues in the making of Indian Smart Cities

Jamshed¹

2018

EAI Endorsed Transactions on Smart Cities

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During the past few years, interest has grown in the creation and application of methods, structures for smart city evaluation. This reflects the increasing awareness of the value of systems for better planning and architecture of efficient and stable communities. A city is a vast and permanent human community that offers many resources and opportunities for its people. The rapid urbanization and growth have put a lot of pressure on local services and service delivery. The research discusses many potential resources that can make a city smart across growing city dimensions and also discusses the initiatives of the Indian government towards a Smart city. This paper is a survey of a variety of papers, covering the actual case study from a more comprehensive framework, such as e-mobility, which covers the smart city subject from.

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“…Since Agrawal et al proposed association rule mining, both data mining and recommendation systems have been developing rapidly. On the one hand, sequential patterns [2], sequential rules [3], coverage patterns [4], temporal patterns [5], subgraph patterns [6] and periodic patterns [7] have been proposed. Data mining, as an increasingly sophisticated technology, has been used for many domains, such as time series analysis [8], medicine [9] and image processing [10].…”

Section: Introductionmentioning

confidence: 99%

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Chen

2021

Entropy

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Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

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Deep learning-based sequential pattern mining for progressive database

Cited by 31 publications

References 35 publications

Mining sequential patterns with flexible constraints from MOOC data

Mining sequential patterns with flexible constraints from MOOC data

An Analysis of Trends, Developments and Transparent Issues in the making of Indian Smart Cities

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

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