Abstract: Existing methods of data mining cannot be applied on
spatial data because they require spatial specificity consideration, as
spatial relationships.
This paper focuses on the classification with decision trees, which
are one of the data mining techniques. We propose an extension of
the C4.5 algorithm for spatial data, based on two different approaches
Join materialization and Querying on the fly the different tables.
Similar works have been done on these two main approaches, the
first - Join materialization - favors the processing time in spite of
memory space, whereas the second - Querying on the fly different
tables- promotes memory space despite of the processing time.
The modified C4.5 algorithm requires three entries tables: a target
table, a neighbor table, and a spatial index join that contains the
possible spatial relationship among the objects in the target table and
those in the neighbor table. Thus, the proposed algorithms are applied
to a spatial data pattern in the accidentology domain.
A comparative study of our approach with other works of
classification by spatial decision trees will be detailed.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.