International Business Machines Corporation

Yasuhiko Morimoto

Takeshi Fukuda

Takeshi Tokuyama

Shinichi Morishita

Shinichi Morishida

Shu Tezuka

Kazuyoshi Hidaka

Akihiro Inokuchi

Harunobu Kubo

Hiromu Ishii

The information display system of the present invention has a database in which objects including position information and attribute information different from the position information are stored, and electronic map storage section, a set forming section which forms at least a certain number of sets of objects positioned in proximity with each other and including a set of objects, a section for selecting the pair of objects contained in each of the sets by locating position coordinates in the electronic map on which display is performed, assigning an indicating figure in variable indicating form, and designating position coordinates for the objects and the indicating figure to be displayed, and a section for placing objects and the indicating figure on the electronic map by using the designated position coordinates.

Information display

A spatial data mining apparatus for calculating an optimal distance from a database, wherein spatial information, such as addresses, is stored, includes an input mechanism, for the input of an objective function required for the optimization of a distance, an intermediate table generator, for employing in the database starting point data and query point data for calculating the distances between each starting point and each query point and for generating an intermediate table, and an optimal distance calculator for calculating a distance based on the intermediate table generated by the intermediate table generator, in order to optimize the value of the objective function that is entered by the input mechanism.

Spatial data mining method, spatial data mining apparatus and storage medium

It is one object of the present invention to perform regional optimization while taking spatial continuity into account, and to provide higher-level spatial data mining. [Constitution] A region calculation method for introducing a two-dimensional association rule, extracted from a database, that includes spatial information, such as addresses, and for applying the two-dimensional association rule for a map, comprises the steps of: defining an objective function that is used to introduce the two-dimensional association rule and that does not include regional information for which an output request has been submitted; dividing a region on the map into pixel grids having a predetermined size to form buckets (S); aggregating data available in the database for each bucket (S); employing the objective function to calculate a region for the optimization of the objective function (S); extracting entities, appearing on the map, that correspond to the obtained region (S); and employing the extracted entities to output a region that is applied for the map.

Region calculation method, spatial data mining apparatus, geographical information display apparatus, spatial data mining system and storage medium

When determining a rule associated with an objective attribute of data in a database to predict the objective attribute value of data, following steps are performed: storing values relative to data belonging to each bucket wherein the values correspond to each bucket in a plane, and the plane has two axes respectively corresponding to two predicative numeric attributes of data and is divided into N&times;M buckets; segmenting a bucket region that is satisfied with a predetermined condition, from the plane; performing a smoothing processing for a boundary of the segmented bucket region; and determining a rule for predicting the object attribute value of the data by the smoothed region.

Method and apparatus for determining rule in database

To generate a more preferable regression tree by using two predicative numeric attributes as a rule for splitting. For N.times.M buckets of a plane that has two axes corresponding to two predicative numeric attributes of data in a database, which belongs to one node of a regression tree, the number u(i, j) of data, belonging to a bucket (coordinate (i, j)), and sum v(i, j) of an objective numeric attribute value of data belonging to the bucket are stored. A bucket region which maximizes the interclass variance is segmented from the plane. Nodes are prepared for the data included in the extracted bucket region and for data outside the bucket region.

Regression tree generation method and apparatus therefor

A method is disclosed for computing an association rule in a database having numerical attributes and 0-1 attributes. First, a numerical attribute is divided into a plurality of intervals (or buckets), and each data set is placed into a single bucket according to the value of the numerical attribute. The number of data sets in each bucket and the number of data sets with a 0-1 attribute being 1 are counted. Second, the starting bucket of an interval to be detected is to be detected. Third, the terminating bucket corresponding to the starting bucket is detected. That is, the largest interval with a confidence equal to or larger than a predetermined value. Fourth, one of the detected pairs of starting and terminating buckets which includes the largest number of customers is the answer to this question. Finally, the required data attributes of data included in this interval is subsequently retrieved.

Method and apparatus for computing association rules for data mining in

A method is disclosed for determining the correlation among data sets having a numerical attribute and a 0-1 attribute. First, a numerical attribute is divided into a plurality of buckets, and each data set is placed into a single bucket according to the value of the numerical attribute. The number of data sets in each bucket and the number of data sets with a 0-1 attribute of 1 are counted. Second, an axis corresponding to the total number of data sets in a first through a particular buckets (X axis) and an axis corresponding to the total number of data sets with a 0-1 attribute of 1 in a first through a particular buckets (Y axis) are virtually established, and points corresponding to the respective values of the first through the particular buckets are virtually plotted. Third, after a plane is constructed in this manner, one of the pairs of points separated at an interval of T.times.N or T or larger which has the largest slope is found. This step is most important to fast processing, and this invention employs the nature of convex hulls to reduce the number of points to be considered. Finally, once this pair of points has been determined, the corresponding pair of buckets can be determined, resulting in the output of the corresponding segment. Also, once this process has been finished, the user can retrieve the required part of the data included in this section.

Method and apparatus for deriving an optimized confidence rule

A method is described for finding correlation between a plurality of data having two kinds of numerical attributes and a true-false attribute. The method comprises the steps of: constituting a plane with two numerical attributes, dividing the plane into meshes, and counting the number of data in each mesh (also called a 'bucket') and the number of data whose true-false attribute represents true. If each mesh is assumed to be a pixel, such plane can be considered as a plane image in which the number of data corresponds to brilliance, and the number of data whose true-false attribute represents true corresponds to saturation. The method further includes the step of segmenting an admissible image which is convex along an axis of the plane according to a predetermined condition .theta. to find an area with strong correlation. If the segmented area as the admissible image satisfies the above-described condition such as the maximized support rule, the method also presents the area to the user. In addition, necessary attributes for data included in the area are also extracted from a database, as required.

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Yamato, Japan

Yasuhiko Morimoto