607 research outputs found
Using Answer Set Programming for pattern mining
Serial pattern mining consists in extracting the frequent sequential patterns
from a unique sequence of itemsets. This paper explores the ability of a
declarative language, such as Answer Set Programming (ASP), to solve this issue
efficiently. We propose several ASP implementations of the frequent sequential
pattern mining task: a non-incremental and an incremental resolution. The
results show that the incremental resolution is more efficient than the
non-incremental one, but both ASP programs are less efficient than dedicated
algorithms. Nonetheless, this approach can be seen as a first step toward a
generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
The need to analyze information from streams arises in a variety of
applications. One of its fundamental research directions is to mine sequential
patterns over data streams. Current studies mine series of items based on the
presence of the pattern in transactions but pay no attention to the series of
itemsets and their multiple occurrences. The pattern over a window of itemsets
stream and their multiple occurrences, however, provides additional capability
to recognize the essential characteristics of the patterns and the
inter-relationships among them that are unidentifiable by the existing
presence-based studies. In this paper, we study such a new sequential pattern
mining problem and propose a corresponding sequential miner with novel
strategies to prune the search space efficiently. Experiments on both real and
synthetic data show the utility of our approach
Inferring neuronal network connectivity from spike data: A temporal data mining approach
Abstract. Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity
Specification mining: Methodologies, theories and applications
Ph.DDOCTOR OF PHILOSOPH
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