STING：一种统计信息网格方法在空间数据挖掘中的应用

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"STING：一种统计信息网格方法用于空间数据挖掘" STING，全称为“Statistical Information Grid”，是一种创新性的方法，旨在处理和分析大量的空间数据，进行空间数据挖掘。这种方法由Wei Wang、Jiong Yang和Richard Muntz在加州大学洛杉矶分校计算机科学系提出。STING的核心目标是解决由于大量空间数据以及需要考虑空间距离的新颖问题所带来的挑战。空间数据挖掘是指在空间数据库中发现可能隐含的有趣特征和模式。由于涉及的数据量庞大，且要考虑空间关系，这是一项复杂的任务。在这一领域，常见的问题包括聚类分析和区域导向查询。过去几年已经提出了一些方法，但它们至少需要对所有个体对象（点）进行一次扫描，因此计算复杂度至少与对象数量成正比，这限制了处理大规模数据的能力。 STING提出了一种分层的统计信息网格策略，它能够高效地处理空间数据，降低查询复杂性。通过构建一个层次化的结构，STING将空间数据组织成网格，每个网格内包含了统计信息，如平均值、方差等，这有助于快速概览数据分布并识别潜在的模式。网格的层次化设计允许快速定位感兴趣的区域，减少了对所有数据进行完整扫描的需求，从而显著提高了查询效率。在STING框架下，聚类分析可以更快地完成，因为只需要处理包含显著特征的网格，而不是每一个单独的对象。同时，区域导向查询也可以通过查找包含特定特征的网格来加速，而不必遍历所有数据点。这种方法降低了计算复杂度，使得在大数据集上执行空间分析成为可能。此外，STING还考虑了空间距离的影响，通过网格间的邻接关系，可以有效地捕捉到空间聚集性和连续性。这种方法在地理信息系统（GIS）、环境科学、城市规划、交通研究等领域具有广泛的应用潜力，因为它能以高效的方式揭示空间数据中的隐藏模式，支持决策者做出基于数据的明智决策。 STING是一种革新性的空间数据挖掘方法，通过统计信息网格提供了一种有效处理大规模空间数据的途径，降低了计算复杂性，增强了聚类和区域查询的性能，为理解和探索复杂的空间现象提供了强大的工具。

STING : A Statistical Information Grid Approach to Spatial Data

Mining

Wei Wang

Department of Computer Science

University of California, Los

Angeles

CA 90095, U.S.A.

weiwang@cs.ucla.edu

Jiong Yang

Department of Computer Science

University of California, Los

Angeles

CA 90095, U.S.A.

jyang@cs.ucla.edu

Richard Muntz

Department of Computer Science

University of California, Los

Angeles

CA 90095, U.S.A.

muntz@cs.ucla.edu

Abstract

1 Introduction

Spatial data mining, i.e., discovery of interesting

characteristics and patterns that may implicitly

exist in spatial databases, is a challenging task

due to the huge amounts of spatial data and to the

new conceptual nature of the problems which

must account for spatial distance. Clustering and

region oriented queries are common problems in

this domain. Several approaches have been

presented in recent years, all of which require at

least one scan of all individual objects (points).

Consequently, the computational complexity is at

least linearly proportional to the number of

objects to answer each query. In this paper, we

propose a hierarchical statistical information grid

based approach for spatial data mining to reduce

the cost further. The idea is to capture statistical

information associated with spatial cells in such a

manner that whole classes of queries and

clustering problems can be answered without

recourse to the individual objects. In theory, and

confirmed by empirical studies, this approach

outperforms the best previous method by at least

an order of magnitude, especially when the data

set is very large.

Permission to copy without fee all or part of this material is

granted provided that the copies are not made or distributed

for direct commercial advantage, the VLDB copyright notice

and the title of the publication and its date appear, and notice

is given that copying is by permission of the Very Large Data

Base Endowment. To copy otherwise, or to republish, requires

a fee and/or special permission from the Endowment.

Proceedings of the 23rd VLDB Conference

Athens, Greece, 1997

In general, spatial data mining, or knowledge discovery in

spatial databases, is the extraction of implicit knowledge,

spatial

relations and discovery of interesting

characteristics and patterns that are not explicitly

represented in the databases. These techniques can play an

important role in understanding spatial data and in

capturing intrinsic relationships between spatial and

nonspatial data. Moreover, such discovered relationships

can be used to present data in a concise manner and to

reorganize

spatial databases to accommodate data

semantics and achieve high performance. Spatial data

mining has wide applications in many fields, including

GIS systems,

image database exploration, medical

imaging, etc.[Che97, Fay96a, Fay96b, Kop96a, Kop96b]

The amount of spatial data obtained from satellite,

medical imagery and other sources has been growing

tremendously in recent years. A crucial challenge in

spatial data mining is the efficiency of spatial data mining

algorithms due to the often huge amount of spatial data

and the complexity of spatial data types and spatial

accessing methods. In this paper, we introduce a new

STatistical INformation Grid-based method (STING) to

efficiently process many common “region oriented”

queries on a set of points. Region oriented queries are

defined later more precisely but informally, they ask for

the selection of regions satisfying certain conditions on

density, total area, etc. This paper is organized as follows.

We first discuss related work in Section 2. We propose

our statistical information grid hierarchical structure and

discuss the query types it can support in Sections 3 and 4,

respectively. The general algorithm as well as a detailed

example of processing a query are given in Section 5. We

analyze the complexity of our algorithm in Section 6. In

Section 7, we analyze the quality of STING’s result and

propose a sufficient condition under which STING is

guaranteed to return the correct result. Limiting Behavior

of STING is in Section 8 and, in Section 9, we analyze the

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