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The International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining (TSDM2000) was held on September 12, 2000 in Lyon, France as a pre-conference workshop of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’2000). Workshop co-chairs were Kathleen Hornsby (University of Maine, USA) and John Roddick (Flinders University, Australia).
Many events repeat themselves as the time goes by. For example, an institute pays its employees on the first day of every month. However, events may not repeat with a constant span of time. In the payday example here, the span of time between each two consecutive paydays ranges between 28 and 31 days. As a result, regularity, or temporal pattern, has to be captured with a use of granularities (such...
In many application areas where databases are mined for classification rules, the latter may be subject to concept drift, that is change over time. Mining without taking this into account can result in severe degradation of the acquired classifier’s performance. This is especially the case when mining is conducted incrementally to maintain knowledge used by an on-line system. The TSAR methodology...
We propose a new center-based iterative clustering algorithm, K- Harmonic Means (KHM), which is essentially insensitive to the initialization of the centers, demonstrated through a set of experiments. The dependency of the K-Means performance on the initialization of the centers has been a major problem; a similar issue exists for an alternative algorithm, Expectation Maximization (EM). Many have...
The novel Time Series Data Mining (TSDM) framework is applied to analyzing financial time series. The TSDM framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. This contrasts with other time series analysis techniques, which typically...
Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique...
A fundamental procedure appearing within such clustering methods as k-Means, Expectation Maximization, Fuzzy-C-Means and Minimum Message Length is that of computing estimators of location. Most estimators of location exhibiting useful robustness properties require at least quadratic time to compute, far too slow for large data mining applications. In this paper, we propose O(Dn√n)-time randomized...
In this paper I define spatio-temporal regions as pairs consisting of a spatial and a temporal component and I define topological relations between them. Using the notion of rough sets I define approximations of spatio-temporal regions and relations between those approximations. Based on relations between approximated spatio-temporal regions configurations of spatio-temporal objects can be characterized...
The growing production of maps is generating huge volume of data stored in large spatial databases. This huge volume of data exceeds the human analysis capabilities. Spatial data mining methods, derived from data mining methods, allow the extraction of knowledge from these large spatial databases, taking into account the essential notion of spatial dependency. This paper focuses on this specificity...
This paper addresses the problem of data mining from temporal data based on calendar (date and time) attributes. The proposed methods uses a probabilistic domain generalization graph, i.e., a graph defining a partial order that represents a set of generalization relations for an attribute, with an associated probability distribution for the values in the domain represented by each of its nodes. We...
Wide spread clustering algorithms use the Euclidean distance to measure spatial proximity. However, obstacles in other GIS data-layers prevent traversing the straight path between two points. AUTOCLUST+ clusters points in the presence of obstacles based on Voronoi modeling and Delaunay Diagrams. The algorithm is free of user-supplied arguments and incorporates global and local variations. Thus, it...
Data mining and knowledge discovery have become important issues for research over the past decade. This has been caused not only by the growth in the size of datasets but also in the availability of otherwise unavailable datasets over the Internet and the increased value that organisations now place on the knowledge that can be gained from data analysis. It is therefore not surprising that the increased...
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