1747696
HOT SAX: efficiently finding the most unusual time series subsequence
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Time series discords have many uses for data mining, including improving the quality of clustering, data cleaning, summarization, and anomaly detection. Discords are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. We evaluate our work with a comprehensive set of experiments. In particular, we demonstrate the utility of discords with objective experiments on domains as diverse as Space Shuttle telemetry monitoring, medicine, surveillance, and industry, and we demonstrate the effectiveness of our discord discovery algorithm with more than one million experiments, on 82 different datasets from diverse domains.
16138730
Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data
In this work, we take the traditional notation of contrast sets and extend them to other data types, in particular time series and by extension, images. In the traditional sense, contrast-set mining identifies attributes, values and instances that differ significantly across groups, and helps user understand the differences between groups of data. We reformulate the notion of contrast-sets for time series data, and define it to be the key pattern(s) that are maximally different from the other set of data. We propose a fast and exact algorithm to find the contrast sets, and demonstrate its utility in several diverse domains, ranging from industrial to anthropology. We show that our algorithm achieves 3 orders of magnitude speedup from the brute-force algorithm, while producing exact solutions.
6501439
Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases
Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequently occurring patterns (motif discovery), surprising patterns (anomaly detection), and query by content. VizTree works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic data sets. Based on the tree structure, we further device a coefficient which measures the dissimilarity between any two time series. This coefficient is shown to be competitive with the well-known Euclidean distance.
6084733
A symbolic representation of time series, with implications for streaming algorithms
The parallel explosions of interest in streaming data, and data mining of time series have had surprisingly little intersection. This is in spite of the fact that time series data are typically streaming data. The main reason for this apparent paradox is the fact that the vast majority of work on streaming data explicitly assumes that the data is discrete, whereas the vast majority of time series data is real valued.Many researchers have also considered transforming real valued time series into symbolic representations, nothing that such representations would potentially allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities, in addition to allowing formerly "batch-only" problems to be tackled by the streaming community. While many symbolic representations of time series have been introduced over the past decades, they all suffer from three fatal flaws. Firstly, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Secondly, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. Finally, most of these symbolic approaches require one to have access to all the data, before creating the symbolic representation. This last feature explicitly thwarts efforts to use the representations with streaming algorithms.In this work we introduce a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. Finally, our representation allows the real valued data to be converted in a streaming fashion, with only an infinitesimal time and space overhead.We will demonstrate the utility of our representation on the classic data mining tasks of clustering, classification, query by content and anomaly detection.
16344930
The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine
Despite its intuitive appeal, the hypothesis that retrieval at the level of "concepts" should outperform purely term-based approaches remains unverified empirically. In addition, the use of "knowledge" has not consistently resulted in performance gains. After identifying possible reasons for previous negative results, we present a novel framework for "conceptual retrieval" that articulates the types of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiments show that an EBM-based scoring algorithm dramatically outperforms a state-of-the-art baseline that employs only term statistics. Ablation studies further yield a better understanding of the performance contributions of different components. Finally, we discuss how other domains can benefit from knowledge-based approaches.
6064636
Automatically Evaluating Answers to Definition Questions
Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called Pourpre, for automatically evaluating answers to definition questions. Until now, the only way to assess the correctness of answers to such questions involves manual determination of whether an information nugget appears in a system's response. The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. Experiments with the TREC 2003 and TREC 2004 QA tracks indicate that rankings produced by our metric correlate highly with official rankings, and that Pourpre outperforms direct application of existing metrics.
15165046
A Classification Tree Based on Discriminant Functions
The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminani functions which are learned using genetic programming. A discriminant function is a mathematical function for classifying data into a specific class. To learn discriminant functions effectively and efficiently, a distance-based fitness function for genetic programming is designed. After the set of discriminant functions for all classes is generated, a classifier is created as a binary decision tree with the Z-value measure to resolve the problem of ambiguity among discriminant functions. Several popular datasets from the UCI Repository were selected to illustrate the effectiveness of the proposed classifiers by comparing with previous methods. The results show that the proposed classification tree demonstrates high accuracy on the selected datasets.
17428031
A Function-Based Classifier Learning Scheme Using Genetic Programming
Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multicategory classifiers based on genetic programming. For a k-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate k discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a Z-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.
5232648
Koala: capture, share, automate, personalize business processes on the web
We present Koala, a system that enables users to capture, share, automate, and personalize business processes on the web. Koala is a collaborative programming-by-demonstration system that records, edits, and plays back user interactions as pseudo-natural language scripts that are both human- and machine-interpretable. Unlike previous programming by demonstration systems, Koala leverages sloppy programming that interprets pseudo-natural language instructions (as opposed to formal syntactic statements) in the context of a given web page's elements and actions. Koala scripts are automatically stored in the Koalescence wiki, where a community of users can share, run, and collaboratively develop their "how-to" knowledge. Koala also takes advantage of corporate and personal data stores to automatically generalize and instantiate user-specific data, so that scripts created by one user are automatically personalized for others. Our initial experiences suggest that Koala is surprisingly effective at interpreting instructions originally written for people.