Mining patterns from data streams: An overview

Borah, Anindita; Nath, Bhabesh

doi:10.1109/i-smac.2017.8058373

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Several challenges such as bounded memory, velocity, concept drift, and enormous volume of data streams [14]. These issues that arise while mining data streams need to be addressed [15]. As indicated from Fig.…”

Section: Research Issues In Mining Frequent Patternsmentioning

confidence: 99%

“…In this model, weights associated with data in the stream depending on its arrival time. This model focuses further on the currently come transactions and gives higher weights to recent data than those in the past [15]. Each transaction of the data stream has a corresponding value, and the value gradually decreases with the increasing time.…”

Section: Damped Window Modelmentioning

confidence: 99%

“…FP-Tree is firstly designed to store transactions in the current time window. Second, the tree pattern structure stores the frequent patterns of previous windows [15].…”

Section: Tilted Time Window Modelmentioning

confidence: 99%

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Frequent Pattern Mining over Streaming Data: From models to research challenges

Saad¹,

Salem

Abdelkader

2021

IJCI. International Journal of Computers and Information

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Research in frequent pattern mining from streaming data becomes a pioneer in the field of information systems. The data stream is a continuous flow of data generated from different sources. Extracting frequent patterns from streaming data raises new challenges for the data mining community. We present an overview of the growing field of data streams. Many applications handle streaming data such as sensor networks, traffic management, log data, telephone call records, and social networks. These applications generate high volumes of streaming data with velocity, which is difficult to handle with traditional data mining techniques. This paper mainly reviewed different research algorithms, scientific practices, and methods that have been developed for mining frequent patterns from streaming data. In addition, it discusses well-known open-source software and tools for data stream mining, which are developing to handle streaming data. Finally, it summarizes the open issues and challenges to current existing approaches while handling and processing data streams in realworld applications.

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Section: Research Issues In Mining Frequent Patternsmentioning

confidence: 99%

Section: Damped Window Modelmentioning

confidence: 99%

See 1 more Smart Citation

Frequent Pattern Mining over Streaming Data: From models to research challenges

Saad¹,

Salem

Abdelkader

2021

IJCI. International Journal of Computers and Information

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“…The notion of pattern mining was introduced, considering the usefulness and applicability of frequent patterns or itemsets present in the database [8]. Transaction databases usually contain huge number of distinct single items whose combination further tends to generate enormous quantity of itemsets [31].…”

Section: Frequent Itemset Generationmentioning

confidence: 99%

Comparative evaluation of pattern mining techniques: an empirical study

Borah

Nath

2020

Complex Intell. Syst.

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Pattern mining has emerged as a compelling field of data mining over the years. Literature has bestowed ample endeavors in this field of research ranging from frequent pattern mining to rare pattern mining. A precise and impartial analysis of the existing pattern mining techniques has therefore become essential to widen the scope of data analysis using the notion of pattern mining. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental algorithms in the field of pattern mining through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare. It provides an analytical comparison of these techniques based on computational time and memory consumption using benchmark real and synthetic data sets. The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories. However, for sparse data sets, level wise approaches performed better than the former ones. This study has been carried out with an aim to analyze the pros and cons of the well known pattern mining techniques under different categories. Through this empirical study, an endeavor has been made to enable the researchers identify some fruitful and promising research directions in one of the most remarkable area of research, pattern mining.

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“…If n sensor flows are synchronized periodically to report their values, the whole of the multivariable information at each time t is represented by the following frame vector (Eq. 1) [6]:…”

Section: Introductionmentioning

confidence: 99%

Identifying data streams anomalies by evolving spiking restricted Boltzmann machines

Xing

Demertzis

Yang

2019

Neural Comput & Applic

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Data streams are characterized by high volatility, and they drastically change in an unpredictable way over time. In the typical case, newer data are the most important, as the concept of aging is based on their timing. These flows require realtime processing in order to extract meaningful information that will allow for essential and targeted responses to changing circumstances. Knowledge mining is a real-time process performed on a subset of the data streams, which contains a small but recent part of the observations. Timely security requirements call for further quest of optimal approaches, capable of improving the reliability and the accuracy of the employed classifiers. This research introduces a real-time evolving spiking restricted Boltzmann machine approach, for efficient anomaly detection in data streams. Testing has proved that the proposed algorithm maximizes the classification accuracy and at the same time minimizes the computational resources requirements. A comparative analysis has shown that it outperforms other data flow analysis algorithms.Keywords Big Data Á Data streams analysis Á Evolving spiking neural networks Á Restricted Boltzmann machines Á Deep learning Á Real-time anomaly detection

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Mining patterns from data streams: An overview

Cited by 5 publications

References 22 publications

Frequent Pattern Mining over Streaming Data: From models to research challenges

Frequent Pattern Mining over Streaming Data: From models to research challenges

Comparative evaluation of pattern mining techniques: an empirical study

Identifying data streams anomalies by evolving spiking restricted Boltzmann machines

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