Density-based clustering of uncertain data

Kriegel, Hans‐Peter; Pfeifle, Martin

doi:10.1145/1081870.1081955

Cited by 241 publications

(153 citation statements)

References 9 publications

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“…The first application on real data showed a significant improvement for the manufacturing yield in comparison to applications using the classical DB-SCAN algorithm. Our next steps include (i) the exhaustive comparison with other algorithms, like [7,9], and (ii) the refinement of our Extended Error-Aware Clustering algorithm.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…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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“…For approximate querying algorithms, the number of sampling instances varies among [10,1000,1000]. We denote BKN s as the number of buckets in DoubleSample technique.…”

Section: Experimental Analysismentioning

confidence: 99%

“…Extensive research effort has been given to model and query uncertain data recently. Research directions include modeling uncertainty [16], query evaluation [2], indexing [18], top-k queries [8], skyline queries [15], clustering and Mining [10], etc. However, though range aggregate query on uncertain data is very important in practice, this problem remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data

Yang

Zhang

et al. 2009

Advances in Data and Web Management

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Abstract. Large amount of uncertain data is inherent in many novel and important applications such as sensor data analysis and mobile data management. A probabilistic threshold range aggregate (PTRA) query retrieves summarized information about the uncertain objects satisfying a range query, with respect to a given probability threshold. This paper is the first one to address this important type of query. We develop a new index structure aU-tree and propose an exact querying algorithm based on aU-tree. For the pursue of efficiency, two techniques SingleSample and DoubleSample are developed. Both techniques provide approximate answers to a PTRA query with accuracy guarantee. Experimental study demonstrates the efficiency and effectiveness of our proposed methods.

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“…A number of discretization algorithms have been proposed in literature, most of them focus on certain data. However, data tends to be uncertain in many applications [9], [10], [11], [12], [13]. Uncertainty can originate from diverse sources such as data collection error, measurement precision limitation, data sampling error, obsolete source, and transmission error.…”

Section: Introductionmentioning

confidence: 99%

A Discretization Algorithm for Uncertain Data

Xia

2010

Lecture Notes in Computer Science

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Abstract. This paper proposes a new discretization algorithm for uncertain data. Uncertainty is widely spread in real-world data. Numerous factors lead to data uncertainty including data acquisition device error, approximate measurement, sampling fault, transmission latency, data integration error and so on. In many cases, estimating and modeling the uncertainty for underlying data is available and many classical data mining algorithms have been redesigned or extended to process uncertain data. It is extremely important to consider data uncertainty in the discretization methods as well. In this paper, we propose a new discretization algorithm called UCAIM (Uncertain Class-Attribute Interdependency Maximization). Uncertainty can be modeled as either a formula based or sample based probability distribution function (pdf). We use probability cardinality to build the quanta matrix of these uncertain attributes, which is then used to evaluate class-attribute interdependency by adopting the redesigned ucaim criterion. The algorithm selects the optimal discretization scheme with the highest ucaim value. Experiments show that the usage of uncertain information helps UCAIM perform well on uncertain data. It significantly outperforms the traditional CAIM algorithm, especially when the uncertainty is high.

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Density-based clustering of uncertain data

Cited by 241 publications

References 9 publications

Error-Aware Density-Based Clustering of Imprecise Measurement Values

Error-Aware Density-Based Clustering of Imprecise Measurement Values

Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data

A Discretization Algorithm for Uncertain Data

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