S-TREE: self-organizing trees for data clustering and online vector quantization

Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Growing self-organizing trees for autonomous hierarchical clustering

This paper presents a new unsupervised learning method based on growing processes and autonomous self-assembly rules. This method, called Growing Self-organizing Trees (GSoT), can grow both network size and tree topology to represent the topological and hierarchical dataset organization, allowing a rapid and interactive visualization. Tree construction rules draw inspiration from elusive properties of biological organization to build hierarchical structures. Experiments conducted on real datasets demonstrate good GSoT performance and provide visual results that are generated during the training process.

MUTUAL INFORMATION CLUSTERING FOR EFFICIENT MINING OF FUZZY ASSOCIATION RULES WITH APPLICATION TO GENE EXPRESSION DATA ANALYSIS

Fuzzy association rules can reveal useful dependencies and interactions hidden in large gene expression data sets. However their derivation perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper follows a divide and conquer approach to the problem that obtains computationally manageable solutions. Initially we cluster genes that more probably are associated. Thereafter, the fuzzy association rule extraction algorithms confront many but significantly reduced computationally problems that usually can be processed very fast.

The clustering phase is accomplished by means of an approach based on mutual information (MI). This approach uses the mutual information as a similarity measure. However, the numerical evaluation of the MI is subtle. We experiment with the main methods and we compare between them. As the device that implements the mutual information clustering we use a SOM (Self-Organized Map) based approach that is capable of effectively incorporating supervised bias.

After the mutual information clustering phase the fuzzy association rules are extracted locally on a per cluster basis. The paper presents an application of the techniques for mining the gene expression data. However, the presented techniques can easily be adapted and can be fruitful for intelligent exploration of any other similar data set as well.

Som-based class discovery exploring the ICA-reduced features of microarray expression profiles

Gene expression datasets are large and complex, having many variables and unknown internal structure. We apply independent component analysis (ICA) to derive a less redundant representation of the expression data. The decomposition produces components with minimal statistical dependence and reveals biologically relevant information. Consequently, to the transformed data, we apply cluster analysis (an important and popular analysis tool for obtaining an initial understanding of the data, usually employed for class discovery). The proposed self-organizing map (SOM)-based clustering algorithm automatically determines the number of ‘natural’ subgroups of the data, being aided at this task by the available prior knowledge of the functional categories of genes. An entropy criterion allows each gene to be assigned to multiple classes, which is closer to the biological representation. These features, however, are not achieved at the cost of the simplicity of the algorithm, since the map grows on a simple grid structure and the learning algorithm remains equal to Kohonen’s one.

The Evolving Tree—Analysis and Applications

In this paper, we enhance and analyze the Evolving Tree (ETree) data analysis algorithm. The suggested improvements aim to make the system perform better while still maintaining the simple nature of the basic algorithm. We also examine the system's behavior with many different kinds of tests, measurements and visualizations. We compare the ETree's performance against classical data analysis methods and very similar modern systems. We find that the ETree is a suitable method for unsupervised analysis of huge data sets.