Pinned Repositories
Associated-Rule-Mining---Brute-Force-Naive-Bayes-FP-Tree
Associated Rule Mining using three different approaches and it's performance analysis: Frequent itemsets are generated using Brute Force, Naive Bayes and FP Tree techniques. Association rule mining is performed on these generated frequent itemsets. Example input csv and txt files are included along with example rules and fptree output files. A modular approach to Associated Rule Mining is demonstrated.
MASR
Pytorch实现的流式与非流式的自动语音识别框架，同时兼容在线和离线识别，目前支持DeepSpeech2模型，支持多种数据增强方法。
EchoInterstellar's Repositories
EchoInterstellar/Associated-Rule-Mining---Brute-Force-Naive-Bayes-FP-Tree
Associated Rule Mining using three different approaches and it's performance analysis: Frequent itemsets are generated using Brute Force, Naive Bayes and FP Tree techniques. Association rule mining is performed on these generated frequent itemsets. Example input csv and txt files are included along with example rules and fptree output files. A modular approach to Associated Rule Mining is demonstrated.
EchoInterstellar/MASR
Pytorch实现的流式与非流式的自动语音识别框架，同时兼容在线和离线识别，目前支持DeepSpeech2模型，支持多种数据增强方法。