# 22.5. Bit Vectors for Representing Sets¶

Determining whether a value is a member of a particular set is a special case of searching for keys in a sequence of records. Thus, any of the search methods discussed in this book can be used to check for set membership. However, we can also take advantage of the restricted circumstances imposed by this problem to develop another representation.

In the case where the set values fall within a limited range, we can represent the set using a bit array with a bit position allocated for each potential member. Those members actually in the set store a value of 1 in their corresponding bit; those members not in the set store a value of 0 in their corresponding bit. For example, consider the set of primes between 0 and 15. Figure 25. shows the corresponding bit array. To determine if a particular value is prime, we simply check the corresponding bit. This representation scheme is called a bit vector or a bitmap. The mark array used in several of the graph algorithms of Chapter Graphs is an example of such a set representation.

If the set fits within a single computer word, then
set union, intersection,
and difference can be performed by logical bit-wise operations.
The union of sets \(A\) and \(B\) is the bit-wise OR function
(whose symbol is `|`

in Java).
The intersection of sets \(A\) and \(B\) is the bit-wise AND
function (whose symbol is `&`

in Java).
For example, if we would like to compute the set of numbers between
0 and 15 that are both prime and odd numbers, we need only compute the
expression

The set difference \(A - B\) can be implemented in Java using
the expression `A&~B`

(`~`

is the symbol for bit-wise negation).
For larger sets that do not fit into a single computer word, the
equivalent operations can be performed in turn on the series of words
making up the entire bit vector.

This method of computing sets from bit vectors is sometimes applied to document retrieval. Consider the problem of picking from a collection of documents those few which contain selected keywords. For each keyword, the document retrieval system stores a bit vector with one bit for each document. If the user wants to know which documents contain a certain three keywords, the corresponding three bit vectors are AND’ed together. Those bit positions resulting in a value of 1 correspond to the desired documents. Alternatively, a bit vector can be stored for each document to indicate those keywords appearing in the document. Such an organization is called a signature file. The signatures can be manipulated to find documents with desired combinations of keywords.