An incremental density-based clustering framework using fuzzy local clustering
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Data stream clustering
Single-linkage clustering
FLAME clustering
Constrained clustering
Clustering high-dimensional data
This study presents a density-based incremental clustering algorithm which incorporates the concept of fuzzy set in clustering. Unlike other existing fuzzy clustering algorithms which are c-mean clustering where the number of clusters must be pre-defined, the proposed algorithm incorporates the concept of fuzzy set into density-based clustering where the number of clusters is not restricted. Moreover, the proposed algorithm uses incremental clustering usually employed in stream data clustering, leading to linear computation time, rather than quadratic computation time as in other density-based clustering. The proposed algorithm outperforms other existing density-based clustering algorithms in terms of both clustering results and computation time. As a result, the proposed algorithm can much efficiently process large data sets than other density-based clustering algorithms.
Data stream clustering
FLAME clustering
Single-linkage clustering
Constrained clustering
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Data stream clustering
Clustering high-dimensional data
Single-linkage clustering
Constrained clustering
DBSCAN
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Clustering is considered as widely used data mining practices. Clustering is the technique of dividing entire dataset in certain clusters created on the comparable characteristics of the instances. On the foundation of the likeness between the instances of data, grouping or clustering the instances of the large database regardless of its size is considered as significant chunk of data mining. There are plentiful approaches of clustering but this book mainly focuses on improving k-Means clustering algorithm. This method clusters the input dataset in quantified number (k) of groups. This method is verified to be very efficient when while dealing with small data, but for huge data, it fails in time complexity; it takes time more than usual. This work mainly aims comparison of k-means clustering scheme with ranking method to speed up the comprehensive running time for k-Means clustering algorithm. The experimental results clearly confirms that the new technique is more time efficient than the old-style k-Means clustering method.
Data stream clustering
Single-linkage clustering
Clustering high-dimensional data
Constrained clustering
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Clustering, an supervised learning process is a challenging problem. Clustering result quality improves the overall structure. In this article, we propose an incremental stream of hierarchical clustering and improve the efficiency, reduce time consumption and accuracy of text categorization algorithm by forming an exact sub clustering. In this paper we propose a new method called multilevel clustering which a combination is of supervised and an unsupervised technique for form the clustering. In this method we form four levels of clustering. The proposed work uses the existing clustering algorithm. We develop and discuss algorithms for multilevel clustering method to achieve the best clustering experiment.
Data stream clustering
Single-linkage clustering
Conceptual clustering
Hierarchical clustering
Brown clustering
Constrained clustering
Consensus clustering
Clustering high-dimensional data
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In the real data world, there are various clustering algorithms available in data mining. The data available from the different data sources may be huge in instances, attributes and in different formats. The clustering algorithms available are assessed based on how the algorithm cluster the given data and find its parametric values. The clustering of data may end in inappropriate results if the algorithm is not chosen wisely. This paper proposes a comparison between diverse clustering algorithms such as K Means clustering, Mini-Batch K Means clustering, Hierarchical clustering, Bagging and Boosting by figuring out clustering strategies using high dimensional datasets on each algorithm above. After the process of data cleaning in dataset, we have clustered the datasets and compared the summary of each to showcase the comparability of difference in their strategical values such as Clustering tendency, clustering quality and data driven approach for evaluating the number of clusters, Normalized Mutual Information (NMI) metric and provide an idea to choose the algorithm for clustering the data effectively. And as a result, Local Clustering Coefficient (LCC) with K-means clustering bunching method performs better than the other clustering algorithms and the results are reported.
Data stream clustering
Single-linkage clustering
Clustering high-dimensional data
Hierarchical clustering
Comparability
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In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density-based clustering algorithm, Partitioning clustering algorithm, Graph-based algorithm, Grid-based algorithm, Model-based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes. This paper is planned to learn and relates various data mining clustering algorithms. Algorithms which are under exploration as follows: K-Means algorithm, K-Medoids, Distributed K-Means clustering algorithm, Hierarchical clustering algorithm, Grid-based Algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.
Data stream clustering
Single-linkage clustering
Hierarchical clustering
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Data stream clustering
Single-linkage clustering
FLAME clustering
Constrained clustering
Clustering high-dimensional data
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The Post-clustering algorithms, which cluster the results of Web search engine, have several different requirements from conventional clustering algorithms. In this paper, we propose the new post-clustering algorithm satisfying those requirements as many as possible. The proposed Concept ART is the form of combining the concept vector that have several advantages in document clustering with Fuzzy ART known as real-time clustering algorithms. Moreover we show that it is applicable to general-purpose clustering as well as post-clustering
Data stream clustering
Single-linkage clustering
Conceptual clustering
Constrained clustering
Brown clustering
Document Clustering
Clustering high-dimensional data
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Clustering high-dimensional data
Data stream clustering
Single-linkage clustering
FLAME clustering
Constrained clustering
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