Outlier Detection on Incremental Data set Using Cell-Based approach in Data Mining

  Dhaval R. Chandarana,  Maulik V. Dhamecha

Outlier detection on uncertain static data is a challenging research problem in data mining. Moreover, the continuous arrival of data makes it more challenging. Hence, in this paper, the problem of outlier detection on Incremental data streams is studied. In particular, we propose a continuous distance-based outlier detection approach on a set of uncertain objects’ states that are originated synchronously from a group of data sources. A set of objects’ states at a timestamp is called a state set. Generally, the duration between two consecutive timestamps is very short and the state of all the objects may not change much in this duration. Therefore, we propose an incremental approach of outlier detection, which makes use of the results obtained from the previous state set to efficiently detect outliers in the current state set. In addition, an approximate incremental outlier detection approach is proposed to further reduce the cost of incremental outlier detection. Finally, the comparison graph of static v/s Incremental data sets is seen in last phase of paper.

Keywords- outlier detection; Incremental data, Static data.

Unique Identification Number - IJEDR1503106
Page Number(s) - 1-6
Pubished in - Volume 3 | Issue 3 | September 2015
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Dhaval R. Chandarana,  Maulik V. Dhamecha,   "Outlier Detection on Incremental Data set Using Cell-Based approach in Data Mining", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 3, pp.1-6, September 2015, Available at :http://www.ijedr.org/papers/IJEDR1503106.pdf