A new cell-based clustering method for large, high-dimensional data in data mining applications

Chang, Jae-Woo; Jin, Du-Seok

doi:10.1145/508884.508886

Cited by 16 publications

(22 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This application of PCA is classified into correlation based clustering methods. Other gridbased approaches include Clustering in QUEst (CLIQUE) introduced by Chang and Jin [31], used for locating dense and spare clusters in subspaces.…”

Section: Distance Based Anomaly Detection Methodsmentioning

confidence: 99%

“…In subspace clustering a cluster is defined as a subset of data objects which are similar to one another in terms of the above defined similarity measures such as distance, density or other such variants for a particular subspace. For example, one of the subspace clustering algorithms, CLustering In QUEst (CLIQUE) proposed by Chang and Jin [31] is used for locating dense and spare clusters in subspaces. Similarly, bi-clusters allow both the objects and attributes to be clustered at the same time allowing a particular object or attribute to be involved in multiple clusters, or not in any cluster at all.…”

Section: If the Object Is A Part Of Small Or Sparse Clustermentioning

confidence: 99%

See 1 more Smart Citation

A survey of data mining and social network analysis based anomaly detection techniques

Kaur

Singh

2016

Egyptian Informatics Journal

View full text Add to dashboard Cite

With the increasing trend of online social networks in different domains, social network analysis has recently become the center of research. Online Social Networks (OSNs) have fetched the interest of researchers for their analysis of usage as well as detection of abnormal activities. Anomalous activities in social networks represent unusual and illegal activities exhibiting different behaviors than others present in the same structure. This paper discusses different types of anomalies and their novel categorization based on various characteristics. A review of number of techniques for preventing and detecting anomalies along with underlying assumptions and reasons for the presence of such anomalies is covered in this paper. The paper presents a review of number of data mining approaches used to detect anomalies. A special reference is made to the analysis of social network centric anomaly detection techniques which are broadly classified as behavior based, structure based and spectral based. Each one of this classification further incorporates number of techniques which are discussed in the paper. The paper has been concluded with different future directions and areas of research that could be addressed and worked upon.Ó 2015 Production and hosting by Elsevier B.V. on behalf

show abstract

Section: Distance Based Anomaly Detection Methodsmentioning

confidence: 99%

Section: If the Object Is A Part Of Small Or Sparse Clustermentioning

confidence: 99%

A survey of data mining and social network analysis based anomaly detection techniques

Kaur

Singh

2016

Egyptian Informatics Journal

View full text Add to dashboard Cite

show abstract

“…Besides the density and grid based algorithms [3,8,13,7,12,16], there are a number of other subspace clustering algorithms that use a top-down strategy to find nonoverlapping subspace clusters [1,2,19,20]. Most of them use greedy or heuristic based approaches, which do not guarantee to find the complete set of subspace clusters.…”

Section: Related Workmentioning

confidence: 99%

“…Many subspace clustering algorithms use a grid and density based approach [3,8,13,7,12]. They partition the data space into non-overlapping rectangular cells by discretizing each dimension into a number of bins.…”

Section: Introductionmentioning

confidence: 99%

“…We get two smaller clusters ({2, 3}, a) and ({2, 4}, {b}). Algorithms have been proposed to find the cutting points adaptively based on data distribution [13,7,12]. However, these algorithms do not allow overlap between different bins either, so it is still possible that objects with similar values on an attribute are placed into different bins, which may cause a cluster to be shattered in different cells.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distance Based Subspace Clustering with Flexible Dimension Partitioning

Liu

Sim

et al. 2007

2007 IEEE 23rd International Conference on Data Engineering

View full text Add to dashboard Cite

Traditional similarity or distance measurements usually become meaningless when the dimensions of the datasets increase, which has detrimental effects on clustering performance. In this paper, we propose a distance-based subspace clustering model, called nCluster, to find groups of objects that have similar values on subsets of dimensions. Instead of using a grid based approach to partition the data space into non-overlapping rectangle cells as in the density based subspace clustering algorithms, the nCluster model uses a more flexible method to partition the dimensions to preserve meaningful and significant clusters. We develop an efficient algorithm to mine only maximal nClusters. A set of experiments are conducted to show the efficiency of the proposed algorithm and the effectiveness of the new model in preserving significant clusters.

show abstract

Efficient mining of distance‐based subspace clusters

Liu

Sim

et al. 2009

Statistical Analysis

View full text Add to dashboard Cite

Traditional similarity measurements often become meaningless when dimensions of datasets increase. Subspace clustering has been proposed to find clusters embedded in subspaces of high dimensional datasets.Many existing algorithms use a grid based approach to partition the data space into nonoverlapping rectangle cells, and then identify connected dense cells as clusters. The rigid boundaries of the grid based approach may cause a real cluster to be divided into several small clusters. In this paper, we propose to use a sliding window approach to partition the dimensions to preserve significant clusters. We call this model nCluster model. The sliding window approach generates more bins than the gridbased approach, thus it incurs higher mining cost. We develop a deterministic algorithm, called MaxnCluster, to mine nClusters efficiently. MaxnCluster uses several techniques to speed up the mining, and it produces only maximal nClusters to reduce result size. Non-maximal nClusters are pruned without the need of storing the discovered nClusters in the memory, which is key to the efficiency of MaxnCluster. Our experiment results show that MaxnCluster can produce maximal nClusters efficiently and accurately.

show abstract

A new cell-based clustering method for large, high-dimensional data in data mining applications

Cited by 16 publications

References 0 publications

A survey of data mining and social network analysis based anomaly detection techniques

A survey of data mining and social network analysis based anomaly detection techniques

Distance Based Subspace Clustering with Flexible Dimension Partitioning

Efficient mining of distance‐based subspace clusters

Contact Info

Product

Resources

About