时空数据挖掘之间的差异

1.空间数据挖掘：
空间数据挖掘是从空间数据库中发现有趣且以前未知但潜在有用的模式的过程。在空间数据挖掘中，分析师使用地理或空间信息来产生商业智能或其他结果。空间数据挖掘涉及的挑战包括识别模式或找到与研究项目相关的对象。

2.时间数据挖掘：
时间数据是指从大量的时间数据集合中提取隐式，非平凡和潜在有用的抽象信息。它关注于时态数据的分析，以及在时态数据挖掘的时态数据集合中找到时态模式和规律性，它们是–

• 数据表征与比较
• 聚类分析
• 分类
• 关联规则
• 预测与趋势分析
• 模式分析

时空数据挖掘之间的区别：

SNO.	Spatial data mining	Temporal data mining
1.	It requires space.	It requires time.
2.	Spatial mining is the extraction of knowledge/spatial relationship and interesting measures that are not explicitly stored in spatial database.	Temporal mining is the extraction of knowledge about occurrence of an event whether they follow Cyclic , Random ,Seasonal variations etc.
3.	It deals with spatial (location , Geo-referenced) data.	It deals with implicit or explicit Temporal content , from large quantities of data.
4.	Spatial databases reverses spatial objects derived by spatial data. types and spatial association among such objects.	Temporal data mining comprises the subject as well as its utilization in modification of fields.
5.	It includes finding characteristic rules, discriminant rules, association rules and evaluation rules etc.	It aims at mining new and unknown knowledge, which takes into account the temporal aspects of data.
6.	It is the method of identifying unusual and unexplored data but useful models from spatial databases.	It deals with useful knowledge from temporal data.
7.	Examples – Determining hotspots , Unusual locations.	Examples – An association rule which looks like – “Any Person who buys a car also buys steering lock”. By temporal aspect this rule would be – ” Any person who buys a car also buys a steering lock after that “.