Outlier Detection with Data Mining Techniques and Statistical Methods Article Swipe
Marcos Orellana
,
Priscila Cedillo
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/inciscos49368.2019.00017
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/inciscos49368.2019.00017
The detection of outliers in the field of data mining (DM) and the process of knowledge discovery in databases (KDD) is of great interest in areas that require support systems for decision making. A straightforward application can be found in the financial area, where DM can potentially detect financial fraud or find errors produced by the users. Thus, it is essential to evaluate the veracity of the information, through the use of methods for the detection of unusual behaviors in the data. This paper proposes a method to detect values that are considered outliers in a database of nominal type data. The method implements a global algorithm of "k" closest neighbors, a clustering algorithm called k-means and a statistical method called chi-square. These techniques have been implemented on a database of clients who have requested a financial credit. The experiment was performed on a data set with 1180 tuples, where, outliers were deliberately introduced. The results showed that the proposed method is able to detect all the outliers entered.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/inciscos49368.2019.00017
- OA Status
- gold
- Cited By
- 5
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3015065616
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https://openalex.org/W3015065616Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/inciscos49368.2019.00017Digital Object Identifier
- Title
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Outlier Detection with Data Mining Techniques and Statistical MethodsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-11-01Full publication date if available
- Authors
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Marcos Orellana, Priscila CedilloList of authors in order
- Landing page
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https://doi.org/10.1109/inciscos49368.2019.00017Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.scipedia.com/public/Orellana_Cedillo_2020aDirect OA link when available
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Outlier, Computer science, Data mining, Anomaly detection, Cluster analysis, Field (mathematics), Context (archaeology), Tuple, Process (computing), Pattern recognition (psychology), Artificial intelligence, Mathematics, Paleontology, Operating system, Discrete mathematics, Biology, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithm | 106, 113 |
| abstract_inverted_index.behaviors | 78 |
| abstract_inverted_index.databases | 18 |
| abstract_inverted_index.detection | 1, 75 |
| abstract_inverted_index.discovery | 16 |
| abstract_inverted_index.essential | 60 |
| abstract_inverted_index.financial | 41, 48, 136 |
| abstract_inverted_index.knowledge | 15 |
| abstract_inverted_index.performed | 141 |
| abstract_inverted_index.requested | 134 |
| abstract_inverted_index.clustering | 112 |
| abstract_inverted_index.considered | 92 |
| abstract_inverted_index.experiment | 139 |
| abstract_inverted_index.implements | 103 |
| abstract_inverted_index.neighbors, | 110 |
| abstract_inverted_index.techniques | 123 |
| abstract_inverted_index.that | 90 |
| abstract_inverted_index.application | 35 |
| abstract_inverted_index.chi-square. | 121 |
| abstract_inverted_index.implemented | 126 |
| abstract_inverted_index.introduced. | 153 |
| abstract_inverted_index.potentially | 46 |
| abstract_inverted_index.statistical | 118 |
| abstract_inverted_index.deliberately | 152 |
| abstract_inverted_index.information, | 67 |
| abstract_inverted_index.straightforward | 34 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.73540213 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |