Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household ...electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.
The genetic cellular response to internal and external changes is determined by the sequence and structure of gene-regulatory promoter regions.
Using data on gene-regulatory elements (i.e., either ...putative or known transcription factor binding sites) and data on gene expression profiles we can discover structural elements in promoter regions and infer the underlying programs of gene regulation. Such hypotheses obtained in silico can greatly assist us in experiment planning. The principal obstacle for such approaches is the combinatorial explosion in different combinations of promoter elements to be examined.
Stemming from several state-of-the-art machine learning approaches we here propose a heuristic, rule-based clustering method that uses gene expression similarity to guide the search for informative structures in promoters, thus exploring only the most promising parts of the vast and expressively rich rule-space.
We present the utility of the method in the analysis of gene expression data on budding yeast S. cerevisiae where cells were induced to proliferate peroxisomes.
We demonstrate that the proposed approach is able to infer informative relations uncovering relatively complex structures in gene promoter regions that regulate gene expression.
Due to more and more on-premises services are migrating onto cloud, user behavioral analysis then gets popular as a data-driven way to administer lots accounts of on-cloud services. This paper ...proposes a novel rule-based approach, GMiner, for mining different types of Google cloud drive usages as an unsupervised account-management approach. Experiment results show that GMiner provides accurate, inter-pretable, and visualized clustering results which are helpful for highlighting inactive, quasi-insider accounts, or other potential cyber-security risks from real-environment dataset.
The complexity of biological networks and the large number of genes in microarray datasets cause a lot of challenges in analyzing gene expression data. Clustering techniques which group the similar ...genes into the same clusters with the purpose of analyzing the function of genes have been used to overcome these challenges. In general, fuzzy clustering methods are more suitable for analyzing gene expression data because of overlap between the biological groups and existing noisy data within the microarray datasets. In this paper by the usage of FRBC(Fuzzy Rule Based Clustering algorithm) approach a fuzzy clustering algorithm is proposed to automatically explore the potential gene clusters in the microarray datasets with no prior knowledge and represent them with some interpretable fuzzy rules that are human understandable. In the simulation results, the accuracy of the algorithm is evaluated on some microarray datasets and to confirm whether the clusters are precisely explored, several validity criteria are used to compare the proposed algorithm with some well-known fuzzy clustering methods.
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