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spatial co-location pattern mining is an important part of spatial data mining, and the purpose is to discover the coexistence spatial feature sets whose instances are frequently located together in a geographic space. However, it ignores the existence of autocorrelation features that is not associated with surrounding features. For example, “cactus” and “jerusalem artichoke” are two common plants...
We present an efficient genetic algorithm for mining multi-objective rules from large databases. Multi-objectives will conflict with each other, which makes it optimization problem that is very difficult to solve simultaneously. We propose a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm(INPGA), which not only accurate selects the candidates but also saves selection...
Clustering in high dimensional data is an important task. Subspace clustering has emerged as a possible solution to the challenges associated with high dimensional clustering. A subspace cluster is a subset of points together with a subset of attributes, such that some category of value of cluster points has great aggregation in these attributes. This paper proposes a subspace clustering algorithm...
When data mining techniques are applied to uncertain data, their uncertainty has to be considered to obtain high quality results. Usually, an uncertain object is described by a probability density function, a probability density function is approximated by a large amount of sample points, and the distance between two uncertain objects is expressed by the expected distance. Computing the expected distance...
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