Big data storage and processing are among the most important challenges now. Among data mining algorithms, DBSCAN is a common clustering method. One of the most important drawbacks of this algorithm is its low execution speed. This study aims to accelerate the DBSCAN execution speed so that the algorithm can respond to big datasets in an acceptable period of time. To overcome the problem, an initial grouping was applied to the data in this article through the K-means++ algorithm. DBSCAN was then employed to perform clustering in each group separately. As a result, the computational burden of DBSCAN execution reduced and the clustering execution speed increased significantly. Finally, border clusters were merged if necessary. According to the results of executing the proposed algorithm, it managed to greatly reduce the DBSCAN execution time (98% in the best-case scenario) with no significant changes in the qualitative evaluation criteria for clustering.

K-DBSCAN: An improved DBSCAN algorithm for big data

In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The grid-based method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality.

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Online Clustering of Evolving Data Streams Using a Density Grid-Based Method

Semantic similarity is the process of identifying relevant data semantically. The traditional way of identifying document similarity is by using synonymous keywords and syntactician. In comparison, semantic similarity is to find similar data using meaning of words and semantics. Clustering is a concept of grouping objects that have the same features and properties as a cluster and separate from those objects that have different features and properties. In semantic document clustering, documents are clustered using semantic similarity techniques with similarity measurements. One of the common techniques to cluster documents is the density-based clustering algorithms using the density of data points as a main strategic to measure the similarity between them. In this paper, a state-of-the-art survey is presented to analyze the density-based algorithms for clustering documents. Furthermore, the similarity and evaluation measures are investigated with the selected algorithms to grasp the common ones. The delivered review revealed that the most used density-based algorithms in document clustering are DBSCAN and DPC. The most effective similarity measurement has been used with density-based algorithms, specifically DBSCAN and DPC, is Cosine similarity with F-measure for performance and accuracy evaluation.

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A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms

The process of converting unstructured data into a structured readable format is becoming hard day by day Till day every organization consists of more than 80% of its operational data in an unreadable format The proposed method helps in converting unreadable data to a readable structured format with the help of Machine learning were classification, and clustering plays a crucial role in converting the operational data into data models and visualize the processed information to the end-user As organizations have specific requirements, considering them, we are going to implement latent dirichlet allocation (LDA) and latent semantic analysis (LSA), which were able to handle discrete data Also, a comparison is made to test divergence, throughput, quality, and response time, as both of them can classify the data based on the content and by giving labels to each category The algorithm with better divergence is implemented that can handle the organizational requirements by presenting the top areas that need to improve/concentrate depending on the analytics made by the algorithm on the available discrete data, and by implementing visualization techniques, the results will be even displayed in graphical format

Effective Comparison of LDA with LSA for Topic Modelling

Density-based clustering is one of the most commonly used analysis methods in data mining and machine learning, with the advantage of locating non-ball-shaped clusters without specifying the number of clusters in advance. However, it has notable shortcomings, such as an inability to distinguish adjacent clusters of different densities. We propose a density-based clustering algorithm, KR-DBSCAN, which is based on the reverse nearest neighbor and influence space. The core objects are identified according to the reverse nearest neighborhood, and their influence spaces are determined by calculating the k-nearest neighborhood and reverse nearest neighborhood for each data object under the Euclidean distance metric. In particular, a new cluster expansion condition is defined using the reverse nearest neighborhood and its influence space, and when the core objects are within their influence spaces, they are added to the cluster by breadth-first traversal. As a result, adjacent clusters with different densities are effectively distinguished, and the computational load is substantially reduced. Boundary objects and noise objects are identified, also using k-nearest neighbors. KR-DBSCAN is experimentally validated on the UCI dataset and some synthetic datasets.

KR-DBSCAN: A density-based clustering algorithm based on reverse nearest neighbor and influence space

Clustering algorithms are a large area of focus in the world of data analytics, especially in the current age of big data sets that are large, multidimensional, and grow quickly. We also live in an age where parallel and distributed computing are at the forefront of any form of data analysis. Because of these two ideas, this paper discusses an algorithm that was designed to scale well with big data sets for fast run time, while also being highly scalable for a potential parallel version. This algorithm, the Fast Density-Grid Clustering Algorithm, works by dividing the data space into a grid structure and then assigning a density measurement to each grid cell. These spaces are then merged based on their densest neighbor in order to cluster the entire space. The experimental results for the serial version demonstrate that the algorithm is generally comparable to DBSCAN in accuracy, while also having a lower run time.

A Fast Density-Grid Based Clustering Method

In recent years there have been many massive leaps in technology that have also resulted in large advancements in how we collect and use data. These advancements have caused a rise in the prominence of the field of Big Data. Organizations and businesses rely heavily on data analysis in almost every field of work. This need for data analysis combined with larger and more complex datasets has caused many challenges for these groups as they seek to keep up. Clustering is a field of data analysis, specifically unsupervised machine learning, that is heavily used in many different industries. Traditional clustering algorithms typically suffer in performance and accuracy as datasets increase in size and dimensionality. We previously proposed a new clustering algorithm called the Fast Density-Grid clustering algorithm that successfully alleviated some of the problems related to runtimes. In modern data analysis however, serial algorithms are still too slow to be of much use. The Fast Density-Grid algorithm was originally designed with parallelization in mind, and this paper discusses the steps taken to implement this. Our experimental results show that, when the number of records in the dataset exceed a certain amount, the parallel form of the algorithm overtakes the traditional in performance. Studying this critical point allows us to determine whether or not the algorithm is suitable for real world use.

A Distributed Density-Grid Clustering Algorithm for Multi-Dimensional Data

Clustering is an unsupervised analytical technique for processing data that works by grouping elements of a set in order to form clusters of similar items. This task lies at the base level of many other tasks including machine vision and artificial intelligence. Big data sets present challenges for clustering due to the size and complexity of the data to be processed. Previous work in this domain resulted in an algorithm called Fast Density-Grid Clustering, which is designed to create a grid structure on the data and then merging cells based on local density. In this paper, we focus on an ongoing modification to this algorithm that could also be used for other density-based clustering algorithms, based on adaptive grids that have an irregular spacing. Testing shows that while the initial results for the first stage of implementation resulted in an average 29.728% loss in accuracy with no significant speed increase, there is a lot of further room for experimentation and development for this approach.

An Attempt at Improving Density-based Clustering Algorithms

The K-Nearest Neighbors (KNN) algorithm is a simple but powerful technique used for data analysis. It identifies existing samples in a dataset which are similar to a new sample by using a distance metric. The new sample can then be classified via a class majority voting of its most similar samples, i.e. nearest neighbors. The KNN algorithm can be applied in many fields, such as recommender systems where it can be used to group related products or predict user preferences. In many cases, the performance of the KNN algorithm diminishes as the size of the dataset increases because the number of comparisons performed increases exponentially. In this paper, we propose a KNN optimization algorithm which leverages vector space models to enhance the nearest neighbors search for a new sample. It accomplishes this enhancement by restricting the search area, and therefore reducing the number of comparisons necessary. The experimental results demonstrate significant performance improvements without compromising the algorithm’s accuracy.

Towards Optimizing Data Analysis for Multi-dimensional Data Sets

We propose an algorithm to improve the cluster analysis process. Clusters are subgroups in a data set that contain data points with similar characteristics within the same subgroup. Cluster analysis is the process of defining and calculating the similarity between data points and grouping them into the same cluster. We present the issues involved in cluster analysis, and present an algorithm to divide the original data set into multiple smaller data sets in the same data space, then perform cluster analysis on each new data set, and finally integrate the clustering results from all the smaller data sets into the final clusters. We discuss various situations where clusters will be discovered in the data space, and conduct experiments and discuss the results of our algorithm.

Daniel Brown

Papers

A Fast Density-Grid Based Clustering Method

A Distributed Density-Grid Clustering Algorithm for Multi-Dimensional Data

An Attempt at Improving Density-based Clustering Algorithms

Towards Optimizing Data Analysis for Multi-dimensional Data Sets

An Attempt to Discover Analytical Information for Multi-Dimensional Data Sets