%0 Journal Article
	%A N. Arulanand and  K. Premalatha
	%D 2014
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 92, 2014
	%T Bin Bloom Filter Using Heuristic Optimization Techniques for Spam Detection
	%U https://publications.waset.org/pdf/9999322
	%V 92
	%X Bloom filter is a probabilistic and memory efficient
data structure designed to answer rapidly whether an element is
present in a set. It tells that the element is definitely not in the set but
its presence is with certain probability. The trade-off to use Bloom
filter is a certain configurable risk of false positives. The odds of a
false positive can be made very low if the number of hash function is
sufficiently large. For spam detection, weight is attached to each set
of elements. The spam weight for a word is a measure used to rate the
e-mail. Each word is assigned to a Bloom filter based on its weight.
The proposed work introduces an enhanced concept in Bloom filter
called Bin Bloom Filter (BBF). The performance of BBF over
conventional Bloom filter is evaluated under various optimization
techniques. Real time data set and synthetic data sets are used for
experimental analysis and the results are demonstrated for bin sizes 4,
5, 6 and 7. Finally analyzing the results, it is found that the BBF
which uses heuristic techniques performs better than the traditional
Bloom filter in spam detection.

	%P 1472 - 1478