A Systematic Review of Density Grid-Based Clustering for Data Streams

Tareq, Mustafa; Sundararajan, Elankovan; Harwood, Aaron; Bakar, Azuraliza Abu

doi:10.1109/access.2021.3134704

Cited by 14 publications

(7 citation statements)

References 112 publications

(206 reference statements)

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“…There is a lack of quantization stage of the raw data before dealing with it in the creation of the core mini clusters. Other algorithms have adopted the grid concept for this quantization, such as MuDi-Stream [26]; however, they used the static grid in time and spatial attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

et al. 2022

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As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.

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Section: Literature Reviewmentioning

confidence: 99%

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

et al. 2022

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“…Although the container does interconnect between the physical database and instance, if any instance fails, the workload manager automatically routes the survivors ensuring availability and reliability of the whole solution model. From the performance perspective, the relevant model is also sharded database introduced in 2017 [25], providing linear scalability, fault tolerance, and geographic data distribution by using horizontal fragmentation across multiple regions [23,26,27]. It is shown in Fig.…”

Section: Database Infrastructurementioning

confidence: 99%

“…Using the parallel option, the table block set is divided into several sub-parts, each assigned to one process run in a parallel mode. Therefore, an index can be created rapidly sooner, depending on the number of CPUs, physical data definition, disc storage allocation, configuration, distribution, and partitioning [26,46,47]. The following code highlights the description of parallelism.…”

Section: Parallel and Nologging Clausementioning

confidence: 99%

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

Kvet

Papán²

2022

IEEE Access

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Data retrieval, access, and tuple identification are inevitable in database processing, ensuring performance. The entities and relationships form a relational database. Data themselves are not specifically formatted, requiring sequential data block scanning if the particular index is unavailable. This paper summarizes existing indexing principles focusing on the B+tree, which forms the default structure for data access based on the index key. Such design is reliable and prone to an increase in the amount of data. However, it cannot manage undefined values properly, whereas they cannot be mathematically compared. Secondly, migrated rows can be present due to the size demand extension after the update operation. Thirdly, the index is always balanced, resulting in additional demands of the transaction. All these factors are covered by the proposed paper, discussing the limitations, opportunities, and own solutions to improve the performance. Several architectures are discussed, maintained, and computationally studied, focusing on the size demands, processing time, and costs. By using proposed techniques, significant improvements can be reached. A pointer list is introduced for migrated row management to reference the index set from the data block and store reference path or using a data reflector. When dealing with transaction support, index management and rebalancing are shifted to the separate autonomous transaction. Thanks to that, the main transaction can be approved sooner with no reliability issues. Finally, the proposed paper introduces the NULL value management structure in the instance memory for the index node reference.

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“…Bahri et al [6] thoroughly investigated the basic algorithms for various data stream mining tasks, including clustering. Tareq et al [7] conducted a systematic literature review on density and grid-based algorithms for clustering data streams by identifying their strengths and weaknesses and investigating how these algorithms address the problems of evolving data streams. Al-Khamees et al [8] classified data stream clustering algorithms into five categories: Partition-based, Hierarchical-based, Gridbased, Density-based, and Model-based.…”

Section: Introductionmentioning

confidence: 99%

Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

Ghani¹,

Aziz²,

AbdulKadir³

2023

Computers, Materials &Amp; Continua

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Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature review on data stream clustering, there is currently no specific review on subspace clustering algorithms in high-dimensional data streams. Therefore, this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments. The review follows a systematic methodological approach and includes 18 articles for the final analysis. The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams. The main findings relate to six elements: clustering process, cluster search, subspace search, synopsis structure, cluster maintenance, and evaluation measures. Most algorithms use a two-phase clustering approach consisting of an initialization stage, a refinement stage, a cluster maintenance stage, and a final clustering stage. The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters. Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers. Future work can focus on the clustering framework, parameter optimization, subspace search techniques, memory-efficient synopsis structures, explicit cluster change detection, and intrinsic performance metrics. This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

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A Systematic Review of Density Grid-Based Clustering for Data Streams

Cited by 14 publications

References 112 publications

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

The Complexity of the Data Retrieval Process Using the Proposed Index Extension

Subspace Clustering in High-Dimensional Data Streams: A Systematic Literature Review

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