Browsing by Subject "Feature hashing"

In the modern era, using personalization when reaching out to potential or current customers is essential for businesses to compete in their area of business. With large customer bases, this personalization becomes more difficult, thus segmenting entire customer bases into smaller groups helps businesses focus better on personalization and targeted business decisions. These groups can be straightforward, like segmenting solely based on age, or more complex, like taking into account geographic, demographic, behavioral, and psychographic differences among the customers. In the latter case, customer segmentation should be performed with Machine Learning, which can help find more hidden patterns within the data. Often, the number of features in the customer data set is so large that some form of dimensionality reduction is needed. That is also the case with this thesis, which includes 12802 unique article tags that are desired to be included in the segmentation. A form of dimensionality reduction called feature hashing is selected for hashing the tags for its ability to be introduced new tags in the future. Using hashed features in customer segmentation is a balancing act. With more hashed features, the evaluation metrics might give better results and the hashed features resemble more closely the unhashed article tag data, but with less hashed features the clustering process is faster, more memory-efficient and the resulting clusters are more interpretable to the business. Three clustering algorithms, K-means, DBSCAN, and BIRCH, are tested with eight feature hashing bin sizes for each, with promising results for K-means and BIRCH.