Corpus ID: 34861087

Discovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report

@inproceedings{Macikag2017DiscoveringSP,
  title={Discovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report},
  author={Piotr S. Macikag},
  year={2017}
}
In the paper, we consider the problem of discovering sequential patterns from event-based spatio-temporal data. The problem is defined as follows: for a set of event types F and for a dataset of events instances D (where each instance in D denotes an occurrence of a particular event type in considered spatio-temporal space), discover all sequential patterns defining the following relation between any event types participating in a particular pattern. The following relation → between any two… Expand

References

SHOWING 1-10 OF 24 REFERENCES
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
TLDR
A novel algorithm called Slicing-STS-miner is proposed to tackle the algorithmic design challenge using the spatial sequence index, which does not preserve the downward closure property and is an order of magnitude faster than STS-Miner for large data sets. Expand
Mining, indexing, and querying historical spatiotemporal data
TLDR
This work defines the spatiotemporal periodic pattern mining problem and proposes an effective and fast mining algorithm for retrieving maximal periodic patterns, and devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotsemporal queries. Expand
Time-focused clustering of trajectories of moving objects
TLDR
This paper proposes an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories, with the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering. Expand
Mining Sequential Patterns: Generalizations and Performance Improvements
TLDR
This work adds time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern, and relax the restriction that the items in an element of a sequential pattern must come from the same transaction. Expand
Algorithms for spatial collocation pattern mining in a limited memory environment: a summary of results
TLDR
This work presents and compares three algorithms for collocation pattern mining in a limited memory environment based on the well-known joinless method introduced by Shekhar and Yoo, and is inspired by a tree structure presented by Wang et al. Expand
Discovering colocation patterns from spatial data sets: a general approach
TLDR
A transaction-free approach to mine colocation patterns by using the concept of proximity neighborhood and a new interest measure, a participation index, is presented which possesses an antimonotone property which can be exploited for computational efficiency. Expand
Spatiotemporal Pattern Mining: Algorithms and Applications
  • Z. Li
  • Geography, Computer Science
  • Frequent Pattern Mining
  • 2014
TLDR
This chapter states the challenges of pattern discovery, reviews the state-of-the-art methods and also discusses the limitations of existing methods. Expand
New Spatiotemporal Clustering Algorithms and their Applications to Ozone Pollution
TLDR
Two new spatiotemporal clustering algorithms, called ST-SNN and ST-SEP-Snn, are proposed, to cluster overlapping polygons that can change their locations, sizes and shapes over time, and both algorithms can find interesting spatiotmporal patterns from ozone pollution data. Expand
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
TLDR
A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth. Expand
Trajectory clustering: a partition-and-group framework
TLDR
A new partition-and-group framework for clustering trajectories is proposed, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster, and a trajectory clustering algorithm TRACLUS is developed, which discovers common sub-trajectories from real trajectory data. Expand
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