Open Access Open Access  Restricted Access Subscription or Fee Access

A Massive Data Reduction Scheme Based on Improved Hausdorff Distance

Shuzhi Nie, Bangyan Ye

Abstract


With the rapid development of information technology, present quite a few massive data, need a lot of time to analyze these complex data, but the current reduction methods also have its drawbacks. In this paper, combine with Hausdorff distance superiority in measuring data sets similarity, propose an improved data reduction method based on local Hausdorff distance, utilize an improved K-NN retrieval algorithm to equably divide the original data sets, obtain a number of sub-data sets, carry out the FCM clustering operation to determine the center point of each category, perform reduction on each sub-data sets, merge each sub-reduction data sets to get the final reduction data sets. The experimental results shown that this method doesn’t rely on experience and knowledge, only need to consider the cases’ distribution in data sets while performing reduction, has a certain amount of universality for case-based data reduction.

Keywords


Massive data reduction, improved Hausdorff distance, K-nearest neighbour retrieval algorithm, Fuzzy C mean clustering algorithm.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.