Incremental generalization for mining in a data warehousing environment

Ester, Martin; Wittmann, Rüdiger

doi:10.1007/bfb0100982

Cited by 157 publications

(257 citation statements)

References 3 publications

Supporting

Mentioning

249

Contrasting

Unclassified

Order By: Relevance

“…clustering) have been proposed in the multidimensional context [19], [10]. In [12], the authors study the generation of fuzzy partitions over numerical dimensions.…”

Section: Methodsmentioning

confidence: 99%

Summarizing Data Cubes Using Blocks

Choong

Laurent

Laurent³

2008

Data Mining Patterns

View full text Add to dashboard Cite

In the context of multidimensional data, OLAP tools are appropriate for the navigation in the data, aiming at discovering pertinent and abstract knowledge. However, due to the size of the data set, a systematic and exhaustive exploration is not feasible. Therefore, the problem is to design automatic tools to ease the navigation in the data and their visualization. In this paper, we present a novel approach allowing to build automatically blocks of similar values in a given data cube that are meant to summarize the content of the cube. Our method is based on a levelwise algorithm (a la Apriori) whose complexity is shown to be polynomial in the number of scans of the data cube. The experiments reported in the paper show that our approach is scalable, in particular in the case where the measure values present in the data cube are discretized using crisp or fuzzy partitions.

show abstract

“…clustering) have been proposed in the multidimensional context [19], [10]. In [12], the authors study the generation of fuzzy partitions over numerical dimensions.…”

Section: Methodsmentioning

confidence: 99%

Summarizing Data Cubes Using Blocks

Choong

Laurent

Laurent³

2008

Data Mining Patterns

View full text Add to dashboard Cite

show abstract

“…For these aggregates, stream sketching techniques [53] may be used to maintain approximate answers. There are also techniques for continuously updating discovered association rules [131] and clusters [43].…”

Section: Profiling Dynamic Datamentioning

confidence: 99%

Profiling relational data: a survey

Abedjan¹,

2015

View full text Add to dashboard Cite

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.

show abstract

“…The idea is that these changes should be recognized in the generated partition without affecting current clusters. In the late nineties, several incremental clustering algorithms have been presented including BIRCH [35], incremental DBSCAN [8] to support data warehousing or Ribert et al's clustering algorithm to generate a hierarchy of clusters [26]. Incremental clustering of text documents has been conducted as a part of the Topic Detection and Tracking initiative [1] to detect a new event from a stream of news articles.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Syed

Kroll

Sabol

et al. 2012

Data Warehousing and Knowledge Discovery

View full text Add to dashboard Cite

Abstract. Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.

show abstract

Incremental generalization for mining in a data warehousing environment

Cited by 157 publications

References 3 publications

Summarizing Data Cubes Using Blocks

Summarizing Data Cubes Using Blocks

Profiling relational data: a survey

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Contact Info

Product

Resources

About