Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

Native Language Identification (NLI) aims at identifying the native languages of authors by analyzing their text samples written in a non-native language. Most existing studies investigate this task for educational applications such as second language acquisition and require the learner corpora. This article performs NLI in a challenging context of the user-generated-content (UGC) where authors are fluent and advanced non-native speakers of a second language. Existing NLI studies with UGC (i) rely on the content-specific/social-network features and may not be generalizable to other domains and datasets, (ii) are unable to capture the variations of the language-usage-patterns within a text sample, and (iii) are not associated with any outlier handling mechanism. Moreover, since there is a sizable number of people who have acquired non-English second languages due to the economic and immigration policies, there is a need to gauge the applicability of NLI with UGC to other languages. Unlike existing solutions, we define a topic-independent feature space, which makes our solution generalizable to other domains and datasets. Based on our feature space, we present a solution that mitigates the effect of outliers in the data and helps capture the variations of the language-usage-patterns within a text sample. Specifically, we represent each text sample as a point set and identify the top- k stylistically similar text samples (SSTs) from the corpus. We then apply the probabilistic k nearest neighbors’ classifier on the identified top- k SSTs to predict the native languages of the authors. To conduct experiments, we create three new corpora where each corpus is written in a different language, namely, English, French , and German . Our experimental studies show that our solution outperforms competitive methods and reports more than 80% accuracy across languages.

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs.

Many algorithms for data mining or indexing time series data do not operate directly on the raw data, but instead they use alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to automatically discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length framework. Extensive empirical tests show that our method is simpler, more general and more accurate than previous methods, and has the important advantage of being essentially parameter-free.

The clustering of glyphs (individual letters/characters/symbols) is typically the first step in document processing algorithms and a critical enabling technology for most historical document indexing techniques. In this work, we take a step back from current domain/language specialized research efforts to consider the problem from an agnostic perspective. In particular, we claim that, independent of the distance measure used, any method that attempts to cluster all the data is almost certainly doomed to failure. We explain this observation, and introduce a clustering method based on Minimum Description Length (MDL) that can overcome it.

As one of the most essential data mining tasks, finding frequently occurring patterns, i.e., motif discovery, has drawn a lot of attention in the past decade. Despite successes in speedup of motif discovery algorithms, most of the existing algorithms still require predefined parameters. The critical and most cumbersome one is time series motif length since it is difficult to manually determine the proper length of the motifs-even for the domain experts. In addition, with variability in the motif lengths, ranking among these motifs becomes another major problem. In this work, we propose a novel algorithm using compression ratio as a heuristic to discover meaningful motifs in proper lengths. The ranking of these various length motifs relies on an ability to compress time series by its own motif as a hypothesis. Furthermore, other than being an anytime algorithm, our experimental evaluation also demonstrates that our proposed method outperforms existing works in various domains both in terms of speed and accuracy.

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

Time series shapelets are a recent promising concept in time series data mining. Shapelets are time series snippets that can be used to classify unlabeled time series. Shapelets not only provide interpretable results, which are useful for domain experts and developers alike, but shapelet-based classifiers have been shown by several independent research groups to have superior accuracy on many datasets. Moreover, shapelets can be seen as generalizing the lazy nearest neighbor classifier to an eager classifier. Thus, as a deployed classification tool, shapelets can be many orders of magnitude faster than any rival with comparable accuracy. Although shapelets are a useful concept, the current literature bemoans the fact that shapelet discovery is a time-consuming task. In spite of several efforts to speed up shapelet discovery algorithms, including the use of specialist hardware, the current state-of-the-art algorithms are still intractable on large datasets. In this work, we propose a fast shapelet discovery algorithm that outperforms the current state-of-the-art by two or three orders of magnitude, while producing models with accuracy that is not perceptibly different.

Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

The recent digitization of more than twenty million books has been led by initiatives from countries wishing to preserve their cultural heritage and by commercial endeavors, such as the Google Print Library Project. Within a few years a significant fraction of the world's books will be online. For millions of intact books and tens of millions of loose pages, the provenance of the manuscripts may be in doubt or completely unknown, thus denying historians an understanding of the context of the content. In some cases it may be possible for human experts to regain the provenance by examining linguistic, cultural and/or stylistic clues. However, such experts are rare and this investigation is clearly a time-consuming process. One technique used by experts to establish provenance is the examination of the ornate initial letters appearing in the questioned manuscript. By comparing the initial letters in the manuscript to annotated initial letters whose origin is known, the provenance can be determined. In this work we show for the first time that we can reproduce this ability with a computer algorithm. We leverage off a recently introduced technique to measure texture similarity and show that it can recognize initial letters with an accuracy that rivals or exceeds human performance. A brute force implementation of this measure would require several years to process a single large book; however, we introduce a novel lower bound that allows us to process the books in minutes.

Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW algorithm faster. For the similarity-search task, an important subroutine in many data mining algorithms, significant progress has been made by replacing the vast majority of expensive DTW calculations with cheap-to-compute lower bound calculations. However, these lower bound based optimizations do not directly apply to clustering, and thus for some realistic problems, clustering with DTW can take days or weeks. In this work, we show that we can mitigate this untenable lethargy by casting DTW clustering as an anytime algorithm. At the heart of our algorithm is a novel data-adaptive approximation to DTW which can be quickly computed, and which produces approximations to DTW that are much better than the best currently known linear-time approximations. We demonstrate our ideas on real world problems showing that we can get virtually all the accuracy of a batch DTW clustering algorithm in a fraction of the time.

Most algorithms for mining or indexing time series data do not operate directly on the original data, but instead they consider alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this paper, we investigate techniques to discover the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length (MDL) framework. Extensive empirical tests show that our method is simpler, more general and significantly more accurate than previous methods, and has the important advantage of being essentially parameter-free.

Given the pervasiveness of time series data in all human endeavors, and the ubiquity of clustering as a data mining application, it is somewhat surprising that the problem of time series clustering from a single stream remains largely unsolved. Most work on time series clustering considers the clustering of individual time series, e.g., gene expression profiles, individual heartbeats or individual gait cycles. The few attempts at clustering time series streams have been shown to be objectively incorrect in some cases, and in other cases shown to work only on the most contrived datasets by carefully adjusting a large set of parameters. In this work, we make two fundamental contributions. First, we show that the problem definition for time series clustering from streams currently used is inherently flawed, and a new definition is necessary. Second, we show that the Minimum Description Length (MDL) framework offers an efficient, effective and essentially parameter-free method for time series clustering. We show that our method produces objectively correct results on a wide variety of datasets from medicine, zoology and industrial process analyses.

The increasing interest in archiving all of humankind's cultural artifacts has resulted in the digitization of millions of books, and soon a significant fraction of the world's books will be online. Most of the data in historical manuscripts is text, but there is also a significant fraction devoted to images. This fact has driven much of the recent increase in interest in query-by-content systems for images. While querying/indexing systems can undoubtedly be useful, we believe that the historical manuscript domain is finally ripe for true unsupervised discovery of patterns and regularities. To this end, we introduce an efficient and scalable system which can detect approximately repeated occurrences of shape patterns both within and between historical texts. We show that this ability to find repeated shapes allows automatic annotation of manuscripts, and allows users to trace the evolution of ideas. We demonstrate our ideas on datasets of scientific and cultural manuscripts dating back to the fourteenth century.