# Subspace Search in Data Streams: Unterschied zwischen den Versionen

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− | |kurzfassung=Modern data mining often takes place on high-dimensional data streams | + | |kurzfassung=Modern data mining often takes place on high-dimensional data streams, which evolve at a very fast pace: On the one hand, the "curse of dimensionality" leads to a sparsely populated feature space, for which classical statistical methods perform poorly. Patterns, such as clusters or outliers, often hide in a few low-dimensional subspaces. On the other hand, data streams are non-stationary and virtually unbounded. Hence, algorithms operating on data streams must work incrementally and take concept drift into account. |

− | + | While "high-dimensionality" and the "streaming setting" provide two unique sets of challenges, we observe that the existing mining algorithms only address them separately. Thus, our plan is to propose a novel algorithm, which keeps track of the subspaces of interest in high-dimensional data streams over time. We quantify the relevance of subspaces via a so-called "contrast" measure, which we are able to maintain incrementally in an efficient way. Furthermore, we propose a set of heuristics to adapt the search for the relevant subspaces as the data and the underlying distribution evolves. | |

− | + | We show that our approach is beneficial as a feature selection method and as such can be applied to extend a range of knowledge discovery tasks, e.g., "outlier detection", in high-dimensional data-streams. | |

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## Version vom 4. Juni 2019, 14:57 Uhr

Vortragende(r) | Florian Kalinke | |
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Vortragstyp | Proposal | |

Betreuer(in) | Edouard Fouché | |

Termin | Fr 19. Juli 2019 | |

Vortragsmodus | ||

Kurzfassung | Modern data mining often takes place on high-dimensional data streams, which evolve at a very fast pace: On the one hand, the "curse of dimensionality" leads to a sparsely populated feature space, for which classical statistical methods perform poorly. Patterns, such as clusters or outliers, often hide in a few low-dimensional subspaces. On the other hand, data streams are non-stationary and virtually unbounded. Hence, algorithms operating on data streams must work incrementally and take concept drift into account.
While "high-dimensionality" and the "streaming setting" provide two unique sets of challenges, we observe that the existing mining algorithms only address them separately. Thus, our plan is to propose a novel algorithm, which keeps track of the subspaces of interest in high-dimensional data streams over time. We quantify the relevance of subspaces via a so-called "contrast" measure, which we are able to maintain incrementally in an efficient way. Furthermore, we propose a set of heuristics to adapt the search for the relevant subspaces as the data and the underlying distribution evolves. We show that our approach is beneficial as a feature selection method and as such can be applied to extend a range of knowledge discovery tasks, e.g., "outlier detection", in high-dimensional data-streams. |