4,097 research outputs found
Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway
The development of Smart Grid in Norway in specific and Europe/US in general
will shortly lead to the availability of massive amount of fine-grained
spatio-temporal consumption data from domestic households. This enables the
application of data mining techniques for traditional problems in power system.
Clustering customers into appropriate groups is extremely useful for operators
or retailers to address each group differently through dedicated tariffs or
customer-tailored services. Currently, the task is done based on demographic
data collected through questionnaire, which is error-prone. In this paper, we
used three different clustering techniques (together with their variants) to
automatically segment electricity consumers based on their consumption
patterns. We also proposed a good way to extract consumption patterns for each
consumer. The grouping results were assessed using four common internal
validity indexes. We found that the combination of Self Organizing Map (SOM)
and k-means algorithms produce the most insightful and useful grouping. We also
discovered that grouping quality cannot be measured effectively by automatic
indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure
LinkCluE: A MATLAB Package for Link-Based Cluster Ensembles
Cluster ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently been introduced with superior performance to the conventional co-association approach. This paper presents a MATLAB package, LinkCluE, that implements the link-based cluster ensemble framework. A variety of functional methods for evaluating clustering results, based on both internal and external criteria, are also provided. Additionally, the underlying algorithms together with the sample uses of the package with interesting real and synthetic datasets are demonstrated herein.
Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach
A significant amount of search queries originate from some real world
information need or tasks. In order to improve the search experience of the end
users, it is important to have accurate representations of tasks. As a result,
significant amount of research has been devoted to extracting proper
representations of tasks in order to enable search systems to help users
complete their tasks, as well as providing the end user with better query
suggestions, for better recommendations, for satisfaction prediction, and for
improved personalization in terms of tasks. Most existing task extraction
methodologies focus on representing tasks as flat structures. However, tasks
often tend to have multiple subtasks associated with them and a more
naturalistic representation of tasks would be in terms of a hierarchy, where
each task can be composed of multiple (sub)tasks. To this end, we propose an
efficient Bayesian nonparametric model for extracting hierarchies of such tasks
\& subtasks. We evaluate our method based on real world query log data both
through quantitative and crowdsourced experiments and highlight the importance
of considering task/subtask hierarchies.Comment: 10 pages. Accepted at SIGIR 2017 as a full pape
Clustering Algorithms: Their Application to Gene Expression Data
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure
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