Page 90 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes order for assembling. Once the final product is ready it is then shipped to customer and all
phases of their assembly processes are thereby recorded into the warehouse management
system. There was no longer a need for paper recording or manual data entry. Using RF
handhelds and barcode technology Naxtor WMS allow Hideaway to track and trace every
item as it is received, put-away, picked and shipped. Each member of the warehouse staff
became an expert on stock and assembly location and warehouse processes.

Hideaway beds at times also required generating their own barcodes, and with Naxtor
WMS with a click of a button the application generates EAN32 and many generic barcode
read by most of the barcode reader. The printed labels are then attached to the incoming
assembly items ready to be put away to their respective locations.
“Having the warehouse system in place means that instead of product location and
warehouse layout sitting in someone’s head, it’s now in the system, and that makes it much
more transferable so that every staff member is an expert,” says Von
After only three week installation and training process, the warehouse was able to leverage
the full capabilities of Naxtor WMS. The company was able to equip warehouse staff with
PSC falcon line or wireless data collection and printing technologies.

4.11 summary

l z Bayesian classifiers are statistical classifiers. They can predict class membership probabilities,
such as the probability that a given sample belongs to a particular class.

l z Bayesian classification is based on Bayes theorem. Bayesian classifiers exhibited high
accuracy and speed when applied to large databases.
l z Bayesian belief networks are graphical models, which unlike naive Bayesian classifiers,
allow the representation of dependencies among subsets of attributes.

l z Bayes’ theorem relates the conditional and marginal probabilities of events A and B, where
B has a non-vanishing probability.

l z Naïve Bayes classifiers assume that the effect of a variable value on a given class is
independent of the values of other variable. This assumption is called class conditional
independence. It is made to simplify the computation and in this sense considered to be
“Naïve”.
l z In theory, Bayesian classifiers have the minimum error rate in comparison to all other
classifiers.

l z Classi fication and prediction are two forms of data analysis, which can be used to extract
models describing important data classes or to predict future data trends. Classification
predicts categorical labels (or discrete values) where as, prediction models continuous-
valued functions.

l z The learning of the model is ‘supervised’ if it is told to which class each training sample
belongs. In contrasts with unsupervised learning (or clustering), in which the class labels
of the training samples are not known, and the number or set of classes to be learned may
not be known in advance.
l z Prediction is similar to classification, except that for prediction, the results lie in the
future.

l z Any of the methods and techniques used for classification may also be used, under
appropriate circumstances, for prediction.

84 LoveLy professionaL university