Clustering high dimensional data is an emerging research field.
Subspace clustering
or
projected clustering
group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and comparison between these paradigms on a common basis.
Conclusive evaluation and comparison is challenged by three major issues. First, there is no ground truth that describes the "true" clusters in real world data. Second, a large variety of evaluation measures have been used that reflect different aspects of the clustering result. Finally, in typical publications authors have limited their analysis to their favored paradigm only, while paying other paradigms little or no attention.
In this paper, we take a systematic approach to evaluate the major paradigms in a common framework. We study representative clustering algorithms to characterize the different aspects of each paradigm and give a detailed comparison of their properties. We provide a benchmark set of results on a large variety of real world and synthetic data sets. Using different evaluation measures, we broaden the scope of the experimental analysis and create a common baseline for future developments and comparable evaluations in the field. For repeatability, all implementations, data sets and evaluation measures are available on our website.
For an increasing number of modern database applications, efficient support of similarity search becomes an important task. Along with the complexity of the objects such as images, molecules and mechanical parts, also the complexity of the similarity models increases more and more. Whereas algorithms that are directly based on indexes work well for simple medium-dimensional similarity distance functions, they do not meet the efficiency requirements of complex high-dimensional and adaptable distance functions. The use of a multi-step query processing strategy is recommended in these cases, and our investigations substantiate that the number of candidates which are produced in the filter step and exactly evaluated in the refinement step is a fundamental efficiency parameter. After revealing the strong performance shortcomings of the state-of-the-art algorithm for
k
-nearest neighbor search [Korn et al. 1996], we present a novel multi-step algorithm which is guaranteed to produce the minimum number of candidates. Experimental evaluations demonstrate the significant performance gain over the previous solution, and we observed average improvement factors of up to 120 for the number of candidates and up to 48 for the total runtime.
Intact Saccharomyces cerevisiae cells were biotinylated with the non-permeable sulfosuccinimidyl-6-(biotinamido)hexanoate reagent. Twenty specifically labelled cell wall proteins could be extracted and visualized on SDS gels via streptavidin/horseradish peroxidase. Nine cell wall proteins were released by SDS extraction under reducing conditions and were designated Scw1-9p for (soluble cell wall proteins); five proteins were released from SDS-extracted cell walls by laminarinase (Ccw1-5p for covalently linked cell wall proteins) and six with mild (30 m-NaOH, 4 C, 14 h) alkali treatment (Ccw6-11p). N-terminal sequences of the Ccw proteins 6, 7, 8 and 11 showed that these cell wall proteins are members of the PIR gene family (predicted proteins with internal repeats), CCW6 being identical to PIR1 and CCW8 to PIR3. Single gene disruptions of all four genes did not yield a phenotype. In the CCW11 disruption the Ccw11p as well as the laminarinase-extracted Ccw5 protein was missing. The new cell wall proteins are O-mannosylated, contain a Kex2 processing site, but no C-terminal GPI anchor sequence.
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