Surprise Detection in Multivariate Astronomical Data

Borne, K. D.; Vedachalam, Arun

doi:10.1007/978-1-4614-3520-4_26

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…In recent decades of years, a variety of researches have proposed different kinds of outlier detection methods. [3] introduced a method based on statistics, which considers an outlier as data whose distribution of distances between itself and its K-nearest neighbors is different from the distribution of distances among the K-nearest neighbors alone. Furthermore, a distance-based method has been put forward in [4], combining the use of random number and pruning function to shorten the running time of the outlier detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

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INOD: A Graph-Based Outlier Detection Algorithm

Zhou

et al. 2013

AMM

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The outlier detection is to select uncommon data from a data set, which can significantly improve the quality of results for the data mining algorithms. A typical feature of the outliers is that they are always far away from a majority of data in the data set. In this paper, we present a graph-based outlier detection algorithm named INOD, which makes use of this feature of the outlier. The DistMean-neighborhood is used to calculate the cumulative in-degree for each data. The data, whose cumulative in-degree is smaller than a threshold, is judged as an outlier candidate. A KNN-based selection algorithm is used to determine the final outlier. Experimental results show that the INOD algorithm can improve the precision 80% higher and decrease the error rate 75% lower than the classical LOF algorithm.

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Section: Introductionmentioning

confidence: 99%

“…The methods above have their own advantages. However, the efficiency of the algorithms [3] [5] is low, which makes them difficult to keep the balance between high precision and low error as data size grows dramatically. The approach in [4] employs a randomized nested-looping algorithm, and is effective for the multi-dimensional data sets.…”

Section: Introductionmentioning

confidence: 99%

INOD: A Graph-Based Outlier Detection Algorithm

Zhou

et al. 2013

AMM

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Virtual Observatories, Data Mining, and Astroinformatics

Borne¹

2013

Planets, Stars and Stellar Systems

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Data mining techniques on astronomical spectra data – II. Classification analysis

Yang

Zhou

Cai

et al. 2022

Monthly Notices of the Royal Astronomical Society

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Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical data processing. However, it is difficult to select an appropriate classification method in practical scenarios due to the different algorithmic ideas and data characteristics. Here, we present the second work in the data mining series - a review of spectral classification techniques. This work also consists of three parts: a systematic overview of current literature, experimental analyses of commonly used classification algorithms and source codes used in this paper. Firstly, we carefully investigate the current classification methods in astronomical literature and organize these methods into ten types based on their algorithmic ideas. For each type of algorithm, the analysis is organized from the following three perspectives. 1) their current applications and usage frequencies in spectral classification are summarized; 2) their basic ideas are introduced and preliminarily analysed; 3) the advantages and caveats of each type of algorithm are discussed. Secondly, the classification performance of different algorithms on the unified datasets is analysed. Experimental data are selected from the LAMOST survey and SDSS survey. Six groups of spectral datasets are designed from data characteristics, data qualities and data volumes to examine the performance of these algorithms. Then the scores of nine basic algorithms are shown and discussed in the experimental analysis. Finally, nine basic algorithms source codes written in python and manuals for usage and improvement are provided.

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Surprise Detection in Multivariate Astronomical Data

Cited by 5 publications

References 22 publications

INOD: A Graph-Based Outlier Detection Algorithm

INOD: A Graph-Based Outlier Detection Algorithm

Virtual Observatories, Data Mining, and Astroinformatics

Data mining techniques on astronomical spectra data – II. Classification analysis

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