User Guided Data Mining Based on Data Syntax and Semantics
Date Issued
2007
Date
2007
Author(s)
Liu, Ken-Hao
DOI
en-US
Abstract
Technology advances in computing powers have enabled many data processing applications and an ever-increasing amount of data is being collected. User guidance on data syntax and semantics is essential to obtain meaningful and useful mining results. Data syntax and semantics come in many forms. In data stream applications, temporal syntax such as sliding windows size based on user preference on the length of query history affect the attainable quality of the synopses under the constraint of fixed storage space budget.
In addition, attribute semantics expressed in user preference ranks affect the usefulness of the clustering analysis. Furthermore, association between semantic units can be explored to boost the performance of classification task. It remains a challenging task to develop effective user-guided data mining techniques based on such data syntax and semantics.
Temporal syntax embedded in the time-evolving data streams can be modeled as sliding windows. Due to the dynamic nature of data streams, a sliding window is used to generate synopses that approximate the most recent data within the retrospective horizon to answer queries or discover patterns. We propose a novel approach, Sliding Dual
Tree, abbreviated as SDT, to generate dynamic synopses that adapt to the insertions and deletions within the retrospective horizon. By exploiting the properties of Haar wavelet transform, we develop several operations to incrementally maintain SDT over consecutive time windows in a time- and space-efficient manner. These operations directly operate on the transformed time-frequency domain without the need of storing/reconstructing the original data. As shown in our thorough analysis, SDT greatly reduces the required resources for synopses generation and maximizes the storage utilization of wavelet synopses in terms of the length of the retrospective horizon and quality measures.
To account for attribute semantics in clustering analysis, conventional clustering algorithms partition the input data set into several clusters by combining all the attributes
of a data tuple to produce the (dis)similarity matrix on a tuple-by-tuple basis. How to explicitly guide clustering based on the user perceptions in a flexible way still remains a challenging task. Therefore, we propose a new clustering framework named Progressive Clustering, which allows the user to express their clustering expectations by assigning ranks to the data attributes. On each rank, the set of attributes with higher or the same rank forms the base space while the set of next highest ranked attributes forms the enhancement space. Then the clustering is carried out in a progressive manner by integrating information in each of the enhancement spaces with the clustering in the base space. The goal of progressive clustering is to generate clusters that are compact in the base space and whose corresponding dissimilarities are minimized in the enhancement space. Therefore, the clustering results conform to user perceptions and become readily accessible for user interpretation.
Concept detection in multimedia data has been proposed recently to deal with the semantic gap in video indexing. Association between semantic units can be viewed as hidden data semantics in concept annotations of video archive. We propose a general postfiltering framework that uses concept association and temporal analysis. We propose an entropy-function based scheme to combine related concept classifiers from the discovered inter-conceptual and temporal association rules. Our empirical studies have shown that our framework is effective in improving the accuracy of visual concept detection.
Subjects
使用者導引
資料處理
資料語義
資料勘測
叢集
資料串流
小波摘要
關聯分析
概念式視訊擷取
user guidance
data processing
data semantics
data mining
clustering
data streams
wavelet synopses
association analysis
concept-based video retrieval
Type
thesis
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