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We introduce a novel technique to detect anomalies in images. The notion of normalcy is given by a baseline of images, under the assumption that the majority of such images is normal. The key of our approach is a featureless probabilistic representation of images, based on the length of the codeword necessary to represent each image. Such codeword's lengths are then used for anomaly detection based...
We propose a probabilistic model for the relevance feedback of users looking for target images. This model takes into account user errors and user uncertainty about distinguishing similarly relevant images. Based on this model, we have developed an algorithm, which selects images to be presented to the user for further relevance feedback until a satisfactory image is found. In each query session,...
Active KDD research groups typically make their software tools at disposal of others through the net. However, integration and reuse of these tools typically require a considerable amount of time to understand software scope and use, install it, transform data in a format compatible with the required input. This paper introduces a semantic based, service-oriented framework for tools sharing and reuse,...
We describe Deimos, a system that automatically discovers and models new sources of information.The system exploits four core technologies developed by our group that makes an end-to-end solution to this problem possible. First, given an example source, Deimos finds other similar sources online. Second, it invokes and extracts data from these sources. Third, given the syntactic structure of a source,...
This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers' movements...
Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset,...
This paper focuses on developing classification algorithms for problems in which there is a need to predict the class based on multiple observations (examples) of the same phenomenon (class). These problems give rise to a new classification problem, referred to as set classification, that requires the prediction of a set of instances given the prior knowledge that all the instances of the set belong...
This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the hidden vector state (HVS) model. The HVS model belongs to the category of statistical learning...
Constraint-based mining has been proven to be extremely useful. It has been applied not only to many pattern discovery settings (e.g., for sequential pattern mining) but also, recently, on classification and clustering tasks (see, e.g., ). It appears as a key technology for an inductive database perspective on knowledge discovery in databases (KDD), and constraint-based mining is indeed an answer...
In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and techniques based on deductive or abductive inference,...
There are several algorithms proposed for maintaining the sequential patterns as records are inserted. In addition to record insertion, the pattern maintenance for record modification is also very important in the real-applications. In the past, we have proposed the fast updated sequential pattern tree (called FUSP tree) structure for handling record insertion. In this paper, we attempt to handle...
The theoretical relationship between association rules and machine learning techniques needs to be studied in more depth. This article studies the use of clustering as a model for association rule mining. The clustering model is exploited to bound and estimate association rule support and confidence. We first study the efficient computation of the clustering model with K-means; we show the sufficient...
In data mining problems, data is usually provided in the form of data tables. To represent knowledge discovered from data tables, decision logic (DL) is proposed in rough set theory. While DL is an instance of propositional logic, we can also describe data tables by other logical formalisms. In this paper, we use a kind of many-sorted logic, called attribute value-sorted logic, to study association...
Relations of logical calculi of association rules to measures of interestingness of association rules are studied. Logical calculi of association rules, 4ft-quantifiers and important classes of association rules are briefly introduced. New 4ft-quantifiers and association rules are defined by applications of suitable thresholds to several known measures of interestingness. It is proved that some of...
Advances in computing and communication has resulted in very large scale distributed environments in recent years. They are capable of storing large volumes of data and often have multiple compute nodes. However, the inherent heterogeneity of data components, the dynamic nature of distributed systems, the need for information synchronization and data fusion over a network and security and access control...
Sequential pattern mining has become more and more popular in recent years due to its wide applications and the fact that it can find more information than association rules. Two famous algorithms in sequential pattern mining are AprioriAll and PrefixSpan. These two algorithms not only need to scan a database or projected-databases many times, but also require setting a minimal support threshold to...
The following topics are dealt with: reliability; knowledge discovery; domain driven data mining; complex data mining; spatial data mining; spatio-temporal data mining; high performance data mining; data mining foundations; semantic aspects; video mining; marketing; design data mining; and dynamic networks.
Constraint-based mining has been proven to be extremely useful for supporting actionable pattern discovery. However, useful conjunctions of constraints that support domain driven mining tasks generally need to set several parameter values and how to tune these parameters remains fairly open. We study this problem for substring pattern discovery, when using a conjunction of maximal frequency, minimal...
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