K-Means is among the most popular and simplest clustering methods. It is intended to partition a data set into a small number of clusters such that feature vectors within a cluster have greater similarity with one another than with feature vectors from other clusters. Each cluster is characterized...
Algorithm - Fulfilled on Amazon SageMaker
Find similar patterns in your time series data based on shape.
Algorithm - Fulfilled on Amazon SageMaker
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer plans to buy Flowers for Valentine’s Day. Based on a set of basic demographics, the model identifies individuals who are likely to be Flower Buyers. The model was trained with data from Prosper's...
Model Package - Fulfilled on Amazon SageMaker
Customer segmentation aggregates the data captured in the KYC details and creates customer groups. The KYC data comes in various categories such as Geographic, Demographic, Behavior and Financial variables. This solution leverages machine learning to identify patterns in the data and segment the...
Model Package - Fulfilled on Amazon SageMaker
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer has a specific health condition. Based on a set of basic demographics, the model identifies individuals who are likely to have the health condition. The model was trained with data from Prosper's...
Model Package - Fulfilled on Amazon SageMaker
Multi-INT Analytics for Pattern Learning and Exploitation (MAPLE) Spatiotemporal Patterns of Life (STPoL) learns normalcy models from observation data. Model clusters capture of where observations usually occur, when they usually occur or how many usually occur. MAPLE's nonparametric machine...
Linux/Unix, CentOS 7 - 64-bit Amazon Machine Image (AMI)
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