Volume 8 Issue 1 - January 2017

  • 1. Spatial data mining for finding nearest neighbor and outlier detection

    Authors : Srishty Jindal, Kamlesh Sharma

    Pages : 30-36

    DOI : http://dx.doi.org/10.21172/1.81.004

    Keywords : spatial database, data mining, outlier detection, nearest neighbor

    Abstract :

    Spatial data mining is a process to extract interesting patterns related to space. Space can be geographic space, the universe, a VLSI design, a molecular structure, or a human body. With the proliferation in use of spatial databases the probability of getting outliers is also increased. These outliers can be noisy data or highly valuable information. If the noise exists in the database, the performance of data mining algorithm may be degraded. Detection of outliers in spatial database can be area of research in various applications. Spatial databases can be used in location based services (e.g. Google maps) to find nearest neighbors. If there is a data point which is not nearer to other data points, then this data point is considered as outlier. In this paper, we have discussed various researches for finding nearest neighbor and outlier detection.

    Citing this Journal Article :

    Srishty Jindal, Kamlesh Sharma, "Spatial data mining for finding nearest neighbor and outlier detection", https://www.ijltet.org/journal_details.php?id=910&j_id=3559, Volume 8 Issue 1 - January 2017, 30-36, #ijltetorg