Hierarchical Density-Based Clustering of Uncertain Data

Kriegel, Hans‐Peter; Pfeifle, Martin

doi:10.1109/icdm.2005.75

Cited by 87 publications

(62 citation statements)

References 2 publications

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“…With this side condition the categorical clustering performs better compared to other approaches. The last class is based on hierarchical clustering and can be found in [16]. Further publications, such as [6,14,17], introduce techniques to enhance the clustering performance but do not introduce a novel concept of clustering or distance functions.…”

Section: Uncertain Clusteringmentioning

confidence: 99%

Clustering Uncertain Data with Possible Worlds

Volk

Rosenthal

Hahmann

et al. 2009

2009 IEEE 25th International Conference on Data Engineering

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The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.

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Section: Uncertain Clusteringmentioning

confidence: 99%

Clustering Uncertain Data with Possible Worlds

Volk

Rosenthal

Hahmann

et al. 2009

2009 IEEE 25th International Conference on Data Engineering

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show abstract

“…Ngai et al [16] propose an algorithm for clustering objects whose location within a bounding hyperrectangle is not known. Kriegel et al propose a method for hierarchical density-based clustering of fuzzy objects [17]. Chiu et al [18] address the problem of mining frequent itemsets, where each item is associated with existential probabilities.…”

Section: Related Workmentioning

confidence: 99%

Using Anonymized Data for Classification

İnan

Kantarcıoğlu

Bertino

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-In recent years, anonymization methods have emerged as an important tool to preserve individual privacy when releasing privacy sensitive data sets. This interest in anonymization techniques has resulted in a plethora of methods for anonymizing data under different privacy and utility assumptions. At the same time, there has been little research addressing how to effectively use the anonymized data for data mining in general and for distributed data mining in particular. In this paper, we propose a new approach for building classifiers using anonymized data by modeling anonymized data as uncertain data. In our method, we do not assume any probability distribution over the data. Instead, we propose collecting all necessary statistics during anonymization and releasing these together with the anonymized data. We show that releasing such statistics does not violate anonymity. Experiments spanning various alternatives both in local and distributed data mining settings reveal that our method performs better than heuristic approaches for handling anonymized data.

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“…However, most of the published data clustering techniques do not consider data objects with imprecise values. Kriegel and Pfeile presented a density-based [7] and a hierarchical density-based clustering approach [8] for uncertain data. They proposed a fuzzy distance function to measure the similarity between fuzzy objects.…”

Section: Related Workmentioning

confidence: 99%

Error-Aware Density-Based Clustering of Imprecise Measurement Values

Yajima¹,

Sato²

2007

Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)

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Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its physical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufacturing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based algorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks.

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Hierarchical Density-Based Clustering of Uncertain Data

Abstract: Abstract

Cited by 87 publications

References 2 publications

Clustering Uncertain Data with Possible Worlds

Clustering Uncertain Data with Possible Worlds

Using Anonymized Data for Classification

Error-Aware Density-Based Clustering of Imprecise Measurement Values

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