Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Case studies of fault diagnosis and energy saving in buildings using data mining techniques
methods for fault detection , diagnostics , and prognostics for building systems — a review , part i .
Collective Contextual Anomaly Detection for Building Energy Consumption
Mining unstructured log files for recurrent fault diagnosis
Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models
Survey of modern fault diagnosis methods in networks
comprehensible software fault and effort prediction : a data mining approach .
Model-based fault detection and diagnosis: status and applications
Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
A review on buildings energy consumption information
GreenDroid: Automated Diagnosis of Energy Inefficiency for Smartphone Applications
Global geometric similarity scheme for feature selection in fault diagnosis
Occupancy-driven energy management for smart building automation
Unsupervised Adaptive Event Detection for Building-Level Energy Disaggregation
Study and analysis of data mining for healthcare
A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches
Finding the different patterns in buildings data using bag of words representation with clustering
Intelligent Building Using Hybrid Inference with Building Automation System to Improve Energy Efficiency
Feature selection techniques for increasing reliability of fault diagnosis of bearings
Fault diagnosis of electronic systems using intelligent techniques: a review
Analysis of Bitcoin Network Dataset for Fraud
Stack-based scheduling of realtime processes
Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity
Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns
An Analysis of Four Missing Data Treatment Methods for Supervised Learning