Spelling suggestions: "subject:"data editing"" "subject:"mata editing""
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An application of the multinomial distribution to the auditing of human encoding errors.Lee, Charles Robert. January 1978 (has links)
Thesis (M.S.)--Ohio State University. / Bibliography: leaves 108-109. Available online via OhioLINK's ETD Center
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Editing and segmenting display files for color graphicsMitchell, Sharlene Kay January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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A modularly expansible minimal multi-screen editorMize, Samuel January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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Parameter estimation when outliers may be present in normal dataQuimby, Barbara Bitz January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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Data editing and logic : the covering set method from the perspective of logic /Boskovitz, Agnes. January 2008 (has links)
Thesis (Ph.D.) -- Australian National University, 2008.
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A full screen editor implemented in PASCALSocolofsky, Theodore John January 2010 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
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Data definition and verification for an integrated database at Marion CollegeBicksler, David Martin 03 June 2011 (has links)
In the transition to a fully integrated database at Marion College in Marion, Indiana, data elements needed more rigorous definitions both in terms of the relationships existing between data elements and attributes assigned to each between files. This data dictionary was thenused to access the attributes of each data element and the links existing and verify data elements within the database to locate illegal and inconsistent data items. From this data verification, weaknesses in procedures and software for loading information into the database were discovered and corrected.Ball State UniversityMuncie, IN 47306
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Toward accurate and efficient outlier detection in high dimensional and large data setsNguyen, Minh Quoc 22 April 2010 (has links)
An efficient method to compute local density-based outliers in high dimensional data was proposed. In our work, we have shown that this type of outlier is present even in any subset of the dataset. This property is used to partition the data set into random subsets to compute the outliers locally. The outliers are then combined from different subsets. Therefore, the local density-based outliers can be computed efficiently. Another challenge in outlier detection in high dimensional data is that the outliers are often suppressed when the majority of dimensions do not exhibit outliers. The contribution of this work is to introduce a filtering method whereby outlier scores are computed in sub-dimensions. The low sub-dimensional scores are filtered out and the high scores are aggregated into the final score. This aggregation with filtering eliminates the effect of accumulating delta deviations in multiple dimensions. Therefore, the outliers are identified correctly. In some cases, the set of outliers that form micro patterns are more interesting than individual outliers. These micro patterns are considered anomalous with respect to the dominant patterns in the dataset. In the area of anomalous pattern detection, there are two challenges. The first challenge is that the anomalous patterns are often overlooked by the dominant patterns using the existing clustering techniques. A common approach is to cluster the dataset using the k-nearest neighbor algorithm. The contribution of this work is to introduce the adaptive nearest neighbor and the concept of dual-neighbor to detect micro patterns more accurately. The next challenge is to compute the anomalous patterns very fast. Our contribution is to compute the patterns based on the correlation between the attributes. The correlation implies that the data can be partitioned into groups based on each attribute to learn the candidate patterns within the groups. Thus, a feature-based method is developed that can compute these patterns efficiently.
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Enabling and supporting the debugging of software failuresClause, James Alexander 21 March 2011 (has links)
This dissertation evaluates the following thesis statement: Program analysis techniques can enable and support the debugging of failures in widely-used applications by (1) capturing, replaying, and, as much as possible, anonymizing failing executions and (2) highlighting subsets of failure-inducing inputs that are likely to be helpful for debugging such failures. To investigate this thesis, I developed techniques for recording, minimizing, and replaying executions captured from users' machines, anonymizing execution recordings, and automatically identifying failure-relevant inputs. I then performed experiments to evaluate the techniques in realistic scenarios using real applications and real failures. The results of these experiments demonstrate that the techniques can reduce the cost and difficulty of debugging.
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Multiple outlier detection and cluster analysis of multivariate normal dataRobson, Geoffrey 12 1900 (has links)
Thesis (MscEng)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: Outliers may be defined as observations that are sufficiently aberrant to arouse the
suspicion of the analyst as to their origin. They could be the result of human error, in
which case they should be corrected, but they may also be an interesting exception,
and this would deserve further investigation.
Identification of outliers typically consists of an informal inspection of a plot of
the data, but this is unreliable for dimensions greater than two. A formal procedure
for detecting outliers allows for consistency when classifying observations. It also
enables one to automate the detection of outliers by using computers.
The special case of univariate data is treated separately to introduce essential
concepts, and also because it may well be of interest in its own right. We then consider
techniques used for detecting multiple outliers in a multivariate normal sample,
and go on to explain how these may be generalized to include cluster analysis.
Multivariate outlier detection is based on the Minimum Covariance Determinant
(MCD) subset, and is therefore treated in detail. Exact bivariate algorithms were
refined and implemented, and the solutions were used to establish the performance
of the commonly used heuristic, Fast–MCD. / AFRIKAANSE OPSOMMING: Uitskieters word gedefinieer as waarnemings wat tot s´o ’n mate afwyk van die verwagte
gedrag dat die analis wantrouig is oor die oorsprong daarvan. Hierdie waarnemings
mag die resultaat wees van menslike foute, in welke geval dit reggestel moet
word. Dit mag egter ook ’n interressante verskynsel wees wat verdere ondersoek
benodig.
Die identifikasie van uitskieters word tipies informeel deur inspeksie vanaf ’n
grafiese voorstelling van die data uitgevoer, maar hierdie benadering is onbetroubaar
vir dimensies groter as twee. ’n Formele prosedure vir die bepaling van uitskieters
sal meer konsekwente klassifisering van steekproefdata tot gevolg hˆe. Dit gee ook
geleentheid vir effektiewe rekenaar implementering van die tegnieke.
Aanvanklik word die spesiale geval van eenveranderlike data behandel om noodsaaklike
begrippe bekend te stel, maar ook aangesien dit in eie reg ’n area van
groot belang is. Verder word tegnieke vir die identifikasie van verskeie uitskieters in
meerveranderlike, normaal verspreide data beskou. Daar word ook ondersoek hoe
hierdie idees veralgemeen kan word om tros analise in te sluit.
Die sogenaamde Minimum Covariance Determinant (MCD) subversameling is
fundamenteel vir die identifikasie van meerveranderlike uitskieters, en word daarom
in detail ondersoek. Deterministiese tweeveranderlike algoritmes is verfyn en ge¨ımplementeer,
en gebruik om die effektiwiteit van die algemeen gebruikte heuristiese algoritme,
Fast–MCD, te ondersoek.
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