Jochen Hipp

Algorithms for association rule mining — a general survey and comparison

ABSTRACT Today there are several eAE ient algorithms that ope with the popular and omputationally expensive task of asso iation rule mining. A tually, these algorithms are more or less des ribed on their own. In this paper we explain the fundamentals of asso iation rule mining and moreover derive a general framework. Based on this we des ribe todays approa hes in ontext by pointing out ommon aspe ts and di eren es. After that we thoroughly investigate their strengths and weaknesses and arry out several runtime experiments. It turns out that the runtime behavior of the algorithms is mu h more similar as to be expe ted.

Making Bertha Drive?An Autonomous Journey on a Historic Route

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Multisensor obstacle detection and tracking

This paper describes a multisensor obstacle detection and tracking system for an autonomous vehicle. The key sensors for obstacle detection are a stereo vision sensor and a laser scanner system. Output from the sensors are fed into a sensor fusion unit which then provides information to the controller.

Video based localization for Bertha

In August 2013, the modified Mercedes-Benz SClass S500 Intelligent Drive (“Bertha”) completed the historic Bertha-Benz-Memorial-Route fully autonomously. The self-driving 103 km journey passed through urban and rural areas. The system used detailed geometric maps to supplement its online perception systems. A map based approach is only feasible if a precise, map relative localization is provided. The purpose of this paper is to give a survey on this corner stone of the system architecture. Two supplementary vision based localization methods have been developed. One of them is based on the detection of lane markings and similar road elements, the other exploits descriptors for point shaped features. A final filter step combines both estimates while handling out-of-sequence measurements correctly.

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

The common approach to exploit mining constraints is to push them deeply into the mining algorithms. In our paper we argue that this approach is based on an understanding of KDD that is no longer up-to-date. In fact, today KDD is seen as a human centered, highly interactive and iterative process. Blindly enforcing constraints already during the mining runs neglects the process character of KDD and therefore is no longer state of the art. Constraints can make a single algorithm run faster but in fact we are still far from response times that would allow true interactivity in KDD. In addition we pay the price of repeated mining runs and moreover risk reducing data mining to some kind of hypothesis testing. Taking all the above into consideration we propose to do exactly the contrary of constrained mining: We accept an initial (nearly) unconstrained and costly mining run. But instead of a sequence of subsequent and still expensive constrained mining runs we answer all further mining queries from this initial result set. Whereas this is straight forward for constraints that can be implemented as filters on the result set, things get more complicated when we restrict the underlying mining data. Actually in practice such constraints are very important, e.g. the generation of rules for certain days of the week, for families, singles, male or female customers etc. We show how to postpone such row-restriction constraints on the transactions from rule generation to rule retrieval from the initial result set.

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

In this paper we deal with association rule mining in the context of a complex, interactive and iterative knowledge discovery process. After a general introduction covering the basics of association rule mining and of the knowledge discovery process in databases we draw the attention to the problematic aspects concerning the integration of both. Actually, we come to the conclusion that with regard to human involvement and interactivity the current situation is far from being satisfying. In our paper we tackle this problem on three sides: First of all there is the algorithmic complexity. Although todays algorithms efficiently prune the immense search space the achieved run times do not allow true interactivity. Nevertheless we present a rule caching schema that significantly reduces the number of mining runs. This schema helps to gain interactivity even in the presence of extreme run times of the mining algorithms. Second, today the mining data is typically stored in a relational database management system. We present an efficient integration with modern database systems which is one of the key factors in practical mining applications. Third, interesting rules must be picked from the set of generated rules. This might be quite costly because the generated rule sets normally are quite large whereas the percentage of useful rules is typically only a very small fraction. We enhance the traditional association rule mining framework in order to cope with this situation.

Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining

Knowledge discovery in databases is a complex, iterative, and highly interactive process. When mining for association rules, typically interactivity is largely smothered by the execution times of the rule generation algorithms. Our approach is to accept a single, possibly expensive run, but all subsequent mining queries are supposed to be answered interactively by accessing a sophisticated rule cache. However there are two critical aspects. First, access to the cache must be efficient and comfortable. Therefore we enrich the basic association mining framework by descriptions of items through application dependent attributes. Furthermore we extend current mining query languages to deal with these attributes through ? and ? quantifiers. Second, the cache must be prepared to answer a broad variety of queries without rerunning the mining algorithm. A main contribution of this paper is that we show how to postpone restrict operations on the transactions from rule generation to rule retrieval from the cache. That is, without actually rerunning the algorithm, we efficiently construct those rules from the cache that would have been generated if the mining algorithm were run on only a subset of the transactions. In addition we describe how we implemented our ideas on a conventional relational database system. We evaluate our prototype concerning response times in a pilot application at DaimlerChrysler. It turns out to satisfy easily the demands of interactive data mining.

MINING FREQUENT TEMPORAL PATTERNS IN INTERVAL SEQUENCES

Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that, at the same time, defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe FSMSet, an algorithm that employs the new support definition, and demonstrate its performance on field data from the automotive business.

Mining sequences of temporal intervals

Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that at the same time: defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe an algorithm that employs the new support definition and demonstrate its performance on field data from the automotive business.

Autonomes Fahren auf der historischen Bertha-Benz-Route

Zusammenfassung Im Jahre 1888 trat Bertha Benz die erste Überlandfahrt in der Geschichte des Automobils an. 125 Jahre später wiederholte die Mercedes Benz S-Klasse S 500 Intelligent Drive diese historische Fahrt von Mannheim nach Pforzheim – selbständig, ohne Fahrereingriff und im realen Verkehr. Die Bertha-Benz-Route ist 103 km lang und zeichnet sich durch eine breite Vielfalt von zu bewältigenden Fahrsituationen aus, die repräsentativ für den heutigen Alltagsverkehr sind. Die Strecke beinhaltet die Innenstädte von Mannheim und Heidelberg sowie die Durchfahrung von 23 Ortschaften und kleineren Städten. Zu den Situationen, die ein autonomes Fahrzeug auf der Bertha-Benz-Route beherrschen muss, gehören z. B. Kreisverkehre, Kreuzungen mit und ohne Ampelanlagen, Zebrastreifen, Überholen von Radfahrern oder enge Ortsdurchfahrten mit entgegenkommendem Verkehr. Eine Besonderheit des vorgestellten Projektes war die ausschließliche Verwendung seriennaher Sensorik. Kameras und Radarsensoren in Verbindung mit einer präzisen digitalen Karte ermöglichten die Erfassung des Fahrzeugumfelds auch in komplexen Situationen. Dieser Artikel liefert eine Systemübersicht des Fahrzeugs. Er beschreibt die kamerabasierte Umgebungswahrnehmung, die verwendeten digitalen Karten und die kartenrelative Selbstlokalisierung sowie die Manöverplanung in komplexen Verkehrsszenarien.