Skip to content
Related Articles

Related Articles

Improve Article
Difference between Spatial and Temporal Data Mining
  • Last Updated : 12 Jun, 2020

1. Spatial Data Mining :
Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. In spatial data mining analyst use geographical or spatial information to produce business intelligence or other results. Challenges involved in spatial data mining include identifying patterns or finding objects that are relevant to research project.

2. Temporal Data Mining :
Temporal data refers to the extraction of implicit, non-trivial and potentially useful abstract information from large collection of temporal data. It is concerned with the analysis of temporal data and for finding temporal patterns and regularities in sets of temporal data tasks of temporal data mining are –

  • Data Characterization and Comparison
  • Cluster Analysis
  • Classification
  • Association rules
  • Prediction and Trend Analysis
  • Pattern Analysis



Difference between Spatial and Temporal Data Mining :

SNO.Spatial data miningTemporal 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 “.

Attention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.

 

My Personal Notes arrow_drop_up
Recommended Articles
Page :