Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time Series Data Mining Algorithms Towards Scalable and Real-Time
  Behavior Monitoring   In recent years, there have been unprecedented technological advances in
sensor technology, and sensors have become more affordable than ever. Thus,
sensor-driven data collection is increasingly becoming an attractive and
practical option for researchers around the globe. Such data is typically
extracted in the form of time series data, which can be investigated with data
mining techniques to summarize behaviors of a range of subjects including
humans and animals. While enabling cheap and mass collection of data,
continuous sensor data recording results in datasets which are big in size and
volume, which are challenging to process and analyze with traditional
techniques in a timely manner. Such collected sensor data is typically
extracted in the form of time series data. There are two main approaches in the
literature, namely, shape-based classification and feature-based
classification. Shape-based classification determines the best class according
to a distance measure. Feature-based classification, on the other hand,
measures properties of the time series and finds the best class according to
the set of features defined for the time series. In this dissertation, we
demonstrate that neither of the two techniques will dominate for some problems,
but that some combination of both might be the best. In other words, on a
single problem, it might be possible that one of the techniques is better for
one subset of the behaviors, and the other technique is better for another
subset of behaviors. We introduce a hybrid algorithm to classify behaviors,
using both shape and feature measures, in weakly labeled time series data
collected from sensors to quantify specific behaviors performed by the subject.
We demonstrate that our algorithm can robustly classify real, noisy, and
complex datasets, based on a combination of shape and features, and tested our
proposed algorithm on real-world datasets.

12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat