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ORCIDDaniyal Kazempour
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2020 – today
- 2024
[j6]Moritz Herrmann
, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. Data Min. Knowl. Discov. 38(3): 840-887 (2024)- [j5]Andreas Lohrer, Daniyal Kazempour, Maximilian Hünemörder, Peer Kröger:
CoMadOut - a robust outlier detection algorithm based on CoMAD. Mach. Learn. 113(10): 8061-8135 (2024) - [c36]Lisa Anders, Daniyal Kazempour, Peer Kröger:
What Will I Need this for Later? Towards a Platform for the Discovery of Intra and Inter-Module Content Relations. CSEDU (2) 2024: 573-580 - [c35]Daniyal Kazempour, Claudius Zelenka, Peer Kröger:
The Missing Link? On the In-Between Instance Detection Task. EDBT 2024: 359-364 - [c34]Andrea Göhring, Mirjam Bayer, Daniyal Kazempour, Sweety Mohanty, Claudius Zelenka:
Data Fusion Between Land and Sea: Multi-Isotope Fingerprints of Viking Animals and Modern Plants. MDM 2024: 319-324 - [i3]Christian Beth, Pamela Fleischmann, Annika Huch, Daniyal Kazempour, Peer Kröger, Andrea Kulow, Matthias Renz:
k-local Graphs. CoRR abs/2410.00601 (2024) - 2023
- [c33]Sweety Mohanty, Daniyal Kazempour, Lavinia Patara, Peer Kröger:
Detection and Tracking of Dynamic Ocean Carbon Uptake Regimes Built Upon Spatial Target-Driver Relationships via Adaptive Hierarchical Clustering. e-Science 2023: 1-10 - [c32]Mirjam Bayer, Tabea Fry, Sören Dethlefsen, Daniyal Kazempour:
Pipeline for open AIS data with filtering based on vessel class. GeoWildLife@SIGSPATIAL 2023: 21-24 - [c31]Claudius Zelenka, Andrea Göhring, Daniyal Kazempour, Maximilian Hünemörder, Lars Schmarje, Peer Kröger:
A Simple and Explainable Method for Uncertainty Estimation using Attribute Prototype Networks. ICCV (Workshops) 2023: 4572-4581 - [c30]Mirjam Raffaela Bayer, Daniyal Kazempour, Peer Kröger:
Towards a fixed-gear AIS trajectory differentiation. SSTD 2023: 61-64 - [c29]Sweety Mohanty, Daniyal Kazempour, Lavinia Patara, Peer Kröger:
Interactive Detection and Visualization of Ocean Carbon Regimes. SSTD 2023: 171-174 - 2022
- [c28]Daniyal Kazempour, Melanie Oelker, Peer Kröger:
Doing groceries again: towards a recommender system for grocery stores selection. LocalRec@SIGSPATIAL 2022: 3:1-3:4 - [i2]Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger:
Enhancing cluster analysis via topological manifold learning. CoRR abs/2207.00510 (2022) - [i1]Andreas Lohrer, Daniyal Kazempour, Maximilian Hünemörder, Peer Kröger:
CoMadOut - A Robust Outlier Detection Algorithm based on CoMAD. CoRR abs/2211.13314 (2022) - 2021
- [j4]Daniyal Kazempour, Johannes Winter, Peer Kröger, Thomas Seidl:
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters. Datenbank-Spektrum 21(3): 213-223 (2021) - [c27]Daniyal Kazempour, Anna Beer, Melanie Oelker, Peer Kröger, Thomas Seidl:
Compound Segmentation via Clustering on Mol2Vec-based Embeddings. e-Science 2021: 60-69 - [e1]Nora Reyes, Richard Connor, Nils M. Kriege, Daniyal Kazempour, Ilaria Bartolini, Erich Schubert, Jian-Jia Chen:
Similarity Search and Applications - 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings. Lecture Notes in Computer Science 13058, Springer 2021, ISBN 978-3-030-89656-0 [contents] - 2020
- [j3]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
"Show Me the Crowds!" Revealing Cluster Structures Through AMTICS. Data Sci. Eng. 5(4): 360-374 (2020) - [c26]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
AMTICS: Aligning Micro-clusters to Identify Cluster Structures. DASFAA (1) 2020: 752-768 - [c25]Daniyal Kazempour, Peer Kröger, Thomas Seidl:
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering. ICDM (Workshops) 2020: 300-307 - [c24]Daniyal Kazempour, Long Mathias Yan, Peer Kröger, Thomas Seidl:
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters. ICDM (Workshops) 2020: 308-315 - [c23]Daniyal Kazempour, Anna Beer, Peer Kröger, Thomas Seidl:
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering. ICDM (Workshops) 2020: 316-323 - [c22]Anna Beer, Daniyal Kazempour, Julian Busch, Alexander Tekles, Thomas Seidl:
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. LWDA 2020: 11-22 - [c21]Felix Borutta, Daniyal Kazempour, Felix Mathy, Peer Kröger, Thomas Seidl:
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform. PAKDD (1) 2020: 356-368
2010 – 2019
- 2019
- [j2]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm. Distributed Parallel Databases 37(1): 39-72 (2019) - [c20]Daniyal Kazempour, Maksim Kazakov, Peer Kröger, Thomas Seidl:
DICE: Density-based Interactive Clustering and Exploration. BTW 2019: 547-550 - [c19]Anna Beer, Daniyal Kazempour, Thomas Seidl:
Rock - Let the points roam to their clusters themselves. EDBT 2019: 630-633 - [c18]Daniyal Kazempour, Lisa Krombholz, Peer Kröger, Thomas Seidl:
A Galaxy of Correlations. EDBT 2019: 702-705 - [c17]Daniyal Kazempour, Thomas Seidl:
Insights into a running clockwork: On interactive process-aware clustering. EDBT 2019: 706-709 - [c16]Anna Beer, Daniyal Kazempour, Marcel Baur, Thomas Seidl:
Human Learning in Data Science. HCI (34) 2019: 170-176 - [c15]Daniyal Kazempour, Anna Beer, Thomas Seidl:
Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. HCI (34) 2019: 184-189 - [c14]Maximilian Archimedes Xaver Hünemörder, Anna Beer, Daniyal Kazempour, Thomas Seidl:
CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA. LWDA 2019: 111-114 - [c13]Daniyal Kazempour, Anna Beer, Oliver Schrüfer, Thomas Seidl:
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019: 127-138 - [c12]Daniyal Kazempour, Long Mathias Yan, Thomas Seidl:
From Covariance to Comode in context of Principal Component Analysis. LWDA 2019: 139-143 - [c11]Maximilian Archimedes Xaver Hünemörder, Daniyal Kazempour, Peer Kröger, Thomas Seidl:
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. SISAP 2019: 221-230 - [c10]Daniyal Kazempour, Max Hünemörder, Thomas Seidl:
On coMADs and Principal Component Analysis. SISAP 2019: 273-280 - [c9]Anna Beer, Daniyal Kazempour, Lisa Stephan, Thomas Seidl:
LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function. SSDBM 2019: 181-184 - [c8]Daniyal Kazempour, Kilian Emmerig, Peer Kröger, Thomas Seidl:
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform. SSDBM 2019: 222-225 - [c7]Daniyal Kazempour, Thomas Seidl:
On systematic hyperparameter analysis through the example of subspace clustering. SSDBM 2019: 226-229 - 2018
- [c6]Daniyal Kazempour, Anna Beer, Friederike Herzog, Daniel Kaltenthaler, Johannes-Y. Lohrer, Thomas Seidl:
FATBIRD: A Tool for Flight and Trajectories Analyses of Birds. eScience 2018: 75-82 - [c5]Daniyal Kazempour, Andrian Mörtlbauer, Peer Kröger, Thomas Seidl:
Mirror Mirror on the Wall, What is the Fairest Linear Parameter Space Representation of All? On Representations of Linear Parameter Space in Context of Clustering. LWDA 2018: 169-173 - [c4]Daniyal Kazempour, Thomas Seidl:
Identifying Entangled Data Points on Iteration Trajectories of Clusterings. LWDA 2018: 174-178 - [c3]Daniyal Kazempour, Kevin Bein, Peer Kröger, Thomas Seidl:
D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space. SISAP 2018: 163-176 - [c2]Daniyal Kazempour, Anna Beer, Johannes-Y. Lohrer, Daniel Kaltenthaler, Thomas Seidl:
PARADISO: an interactive approach of parameter selection for the mean shift algorithm. SSDBM 2018: 26:1-26:4 - 2017
- [c1]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting Global Hyperparaboloid Correlated Clusters Based on Hough Transform. SSDBM 2017: 31:1-31:6 - 2013
- [j1]Kai Blin, Marnix H. Medema, Daniyal Kazempour, Michael A. Fischbach, Rainer Breitling, Eriko Takano, Tilmann Weber:
antiSMASH 2.0 - a versatile platform for genome mining of secondary metabolite producers. Nucleic Acids Res. 41(Webserver-Issue): 204-212 (2013)
Coauthor Index
aka: Maximilian Archimedes Xaver Hünemörder
aka: Maximilian Hünemörder
aka: Max Hünemörder
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