This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
DES( Data Encryption Standard)算法，于1977年得到美國政府的正式許可，是一種用56位密鑰來加密64位數據的方法。雖然56位密鑰的DES算法已經風光不在,而且常有用Des加密的明文被破譯的報道,但是了解一下昔日美國的標準加密算法總是有益的,而且目前DES算法得到了廣泛的應用
SOUNDSC(Y,...) is the same as SOUND(Y,...) except the data is
scaled so that the sound is played as loud as possible without
clipping. The mean of the data is removed.
