<p>That's a subcategory problem within machine learning. You can learn a lot reading this survey: "One-Class Classification: Taxonomy of Study and Review of
Techniques" (<a href="http://arxiv.org/pdf/1312.0049.pdf" rel="nofollow">http://arxiv.org/pdf/1312.0049.pdf</a>). Hope it helps.</p>
Learn to predict future customer's behavior from history
<p>I have a dataset which contains visiting history from customers. </p>

<p>It has three columns in dataset including customer ID, AM/PM (visit at AM or PM) and Weekday/Weekend (visit on weekday or weekend). </p>

<p>I want to learn from this dataset and select the top 50 customers who have the biggest chance to visit in specified input (like AM / Weekday).</p>

<p>For now, I create model for each customer by using one-class SVM (I only have positive (visit) data). Since the one-class SVM only has binary output, I can only tell the certain customer will visit or not in specified input, rather than selecting the top 50 customers.</p>

<p>I was wondering if there is an algorithm that can learn from a positive-only dataset and give a score or probability like output?</p>