Jochen Hipp

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Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches

Ulrich Güntzer, Jochen Hipp, Gholamreza Nakhaeizadeh, 2000, PKDD.

Is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule mining

Ulrich Güntzer, Jochen Hipp, Ulrich Güntzer, 2002, SKDD.

A New Algorithm for Faster Mining of Generalized Association Rules

Rüdiger Wirth, Andreas Myka, Ulrich Güntzer, 1998, PKDD.

Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets pdf

Jochen Hipp, Manfred Reichert, Julian Schlechtriemen, 2019, IEEE Transactions on Intelligent Transportation Systems.

Wissensentdeckung in Datenbanken mit Assoziationsregeln

Jochen Hipp, J. Hipp, 2003 .

Regelbasierte Ausreißersuche zur Datenqualitätsanalyse

Udo Grimmer, Jochen Hipp, Joachim Kübart, 2005, Datenbank-Spektrum.

Outlier Detection by Rareness Assumption

Jochen Hipp, Tomas Hrycej, T. Hrycej, 2004, GI Jahrestagung.

Mining Sequences of Temporal Intervals

Jochen Hipp, Steffen Kempe, Jochen Hipp, 2006, PKDD.

Data Mining of Association Rules and the Process of Knowledge Discovery in Databases

Ulrich Güntzer, Jochen Hipp, Gholamreza Nakhaeizadeh, 2002, Industrial Conference on Data Mining.

Method for checking a digital map

Martin Haueis, Jochen Hipp, Martin Hein, 2015 .

Data Mining: The Next Generation

Hans-Peter Kriegel, Jaideep Srivastava, Daniel A. Keim, 2004 .

Data Quality Mining - Making a Virute of Necessity

Ulrich Güntzer, Udo Grimmer, Jochen Hipp, 2001, DMKD.

Multisensor obstacle detection and tracking

Christoph Stiller, Jochen Hipp, C. Rössig, 2000, Image Vis. Comput..

Integrating Association Rule Mining Algorithms with Relational Database Systems

Ulrich Güntzer, Udo Grimmer, Jochen Hipp, 2001, ICEIS.

Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Manfred Reichert, Jochen Hipp, Julian Schlechtriemen, 2020, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

CPD: Crowd-based Pothole Detection

Manfred Reichert, Kai-Uwe Sattler, Jochen Hipp, 2019, VEHITS.

Interactivity Closes the Gap - Lessons Learned in an Automotive Industry Application

Rüdiger Wirth, Markus Mueller, Carsten Lanquillon, 2010, Data Mining for Business Applications.

Making Bertha Drive—An Autonomous Journey on a Historic Route

Julius Ziegler, Nils Appenrodt, Carsten Brenk, 2014, IEEE Intelligent Transportation Systems Magazine.

When Distribution is Part of the Semantics: A New Problem Class for Distributed Knowledge Discovery

Rüdiger Wirth, Jochen Hipp, Michael Borth, 2001 .

Video based localization for Bertha

Julius Ziegler, Christoph Gustav Keller, Henning Lategahn, 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

Wissensentdeckung in Datenbanken mit Assoziationsregeln: Verfahren zur effizienten Regelgenerierung und deren Integration in den Wissensentdeckungsprozeß

Jochen Hipp, 2003 .

Mining Frequent Temporal Patterns in Interval Sequences

Rudolf Kruse, Carsten Lanquillon, Jochen Hipp, 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining

Christoph Mangold, Ulrich Güntzer, Jochen Hipp, 2002, PAKDD.

Rule-Based Measurement Of Data Quality In Nominal Data

Felix Naumann, Markus Müller, Jochen Hipp, 2007, ICIQ.

Analysing Warranty Claims of Automobiles; An Application Description Following the CRISP-DM Data Mining Process

Guido Lindner, Jochen Hipp, Jochen Hipp, 1999, ICSC.

Algorithms for association rule mining — a general survey and comparison

Ulrich Güntzer, Jochen Hipp, Gholamreza Nakhaeizadeh, 2000, SKDD.

Deriving HD maps for highly automated driving from vehicular probe data

Ilja Radusch, Birgit Kwella, Martin Haueis, 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

Autonomes Fahren auf der historischen Bertha-Benz-Route

Martin Lauer, Julius Ziegler, Nils Appenrodt, 2015 .

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions

Manfred Reichert, Jochen Hipp, Julian Schlechtriemen, 2020, 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns

Rudolf Kruse, Jochen Hipp, Steffen Kempe, 2007, GfKl.

CRISP-DM: Towards a Standard Process Model for Data Mining

Rüdiger Wirth, Jochen Hipp, R. Wirth, 2000 .

Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets

Manfred Reichert, Jochen Hipp, Julian Schlechtriemen, 2021, IEEE Transactions on Intelligent Transportation Systems.

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks

Manfred Reichert, Jochen Hipp, Julian Schlechtriemen, 2021, IEEE Robotics and Automation Letters.

The Atlas of Lane Changes: Investigating Location-Dependent Lane Change Behaviors Using Measurement Data from a Customer Fleet

Manfred Reichert, Jochen Hipp, Florian Wirthmüller, 2021, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).