Exploiting Homogeneity of Density in Incremental Hierarchical Clustering

Hierarchical clustering is an important tool in many applications. As it involves a large data set that proliferates over time, reclustering the data set periodically is not an efficient process. Therefore, the ability to incorporate a new data set incrementally into an existing hierarchy becomes increasingly demanding. This article describes HOMOGEN, a system that employs a new algorithm for generating a hierarchy of concepts and clusters incrementally from a stream of observations. The system aims to construct a hierarchy that satisfies the homogeneity and the monotonicity properties. Working in a bottom-up fashion, a new observation is placed in the hierarchy and a sequence of hierarchy restructuring processes is performed only in regions that have been affected by the presence of the new observation. Additionally, it combines multiple restructuring techniques that address different restructuring objectives to get a synergistic effect. The system has been tested on a variety of domains including structured and unstructured data sets. The experimental results reveal that the system is able to construct a concept hierarchy that is consistent regardless of the input data order and whose quality is comparable to the quality of those produced by non incremental clustering algorithms

Exploiting Homogeneity of Density in Incremental Hierarchical Clustering

Hierarchical clustering is an important tool in many applications. As it involves a large data set that proliferates over time, reclustering the data set periodically is not an efficient process. Therefore, the ability to incorporate a new data set incrementally into an existing hierarchy becomes increasingly demanding. This article describes HOMOGEN, a system that employs a new algorithm for generating a hierarchy of concepts and clusters incrementally from a stream of observations. The system aims to construct a hierarchy that satisfies the homogeneity and the monotonicity properties. Working in a bottom-up fashion, a new observation is placed in the hierarchy and a sequence of hierarchy restructuring processes is performed only in regions that have been affected by the presence of the new observation. Additionally, it combines multiple restructuring techniques that address different restructuring objectives to get a synergistic effect. The system has been tested on a variety of domains including structured and unstructured data sets. The experimental results reveal that the system is able to construct a concept hierarchy that is consistent regardless of the input data order and whose quality is comparable to the quality of those produced by non incremental clustering algorithms

Iterative Optimization and Simplification of Hierarchical Clusterings

Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts -- often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to `externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.Comment: See http://www.jair.org/ for any accompanying file

Concept drift learning and its application to adaptive information filtering

Tracking the evolution of user interests is a problem instance of concept drift learning. Keeping track of multiple interest categories is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. The first part of this dissertation presents a Multiple Three-Descriptor Representation (MTDR) algorithm, a novel algorithm for learning concept drift especially built for tracking the dynamics of multiple target concepts in the information filtering domain. The learning process of the algorithm combines the long-term and short-term interest (concept) models in an attempt to benefit from the strength of both models. The MTDR algorithm improves over existing concept drift learning algorithms in the domain. Being able to track multiple target concepts with a few examples poses an even more important and challenging problem because casual users tend to be reluctant to provide the examples needed, and learning from a few labeled data is generally difficult. The second part presents a computational Framework for Extending Incomplete Labeled Data Stream (FEILDS). The system modularly extends the capability of an existing concept drift learner in dealing with incomplete labeled data stream. It expands the learner's original input stream with relevant unlabeled data; the process generates a new stream with improved learnability. FEILDS employs a concept formation system for organizing its input stream into a concept (cluster) hierarchy. The system uses the concept and cluster hierarchy to identify the instance's concept and unlabeled data relevant to a concept. It also adopts the persistence assumption in temporal reasoning for inferring the relevance of concepts. Empirical evaluation indicates that FEILDS is able to improve the performance of existing learners particularly when learning from a stream with a few labeled data. Lastly, a new concept formation algorithm, one of the key components in the FEILDS architecture, is presented. The main idea is to discover intrinsic hierarchical structures regardless of the class distribution and the shape of the input stream. Experimental evaluation shows that the algorithm is relatively robust to input ordering, consistently producing a hierarchy structure of high quality

Concept drift learning and its application to adaptive information filtering

Tracking the evolution of user interests is a problem instance of concept drift learning. Keeping track of multiple interest categories is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. The first part of this dissertation presents a Multiple Three-Descriptor Representation (MTDR) algorithm, a novel algorithm for learning concept drift especially built for tracking the dynamics of multiple target concepts in the information filtering domain. The learning process of the algorithm combines the long-term and short-term interest (concept) models in an attempt to benefit from the strength of both models. The MTDR algorithm improves over existing concept drift learning algorithms in the domain. Being able to track multiple target concepts with a few examples poses an even more important and challenging problem because casual users tend to be reluctant to provide the examples needed, and learning from a few labeled data is generally difficult. The second part presents a computational Framework for Extending Incomplete Labeled Data Stream (FEILDS). The system modularly extends the capability of an existing concept drift learner in dealing with incomplete labeled data stream. It expands the learner's original input stream with relevant unlabeled data; the process generates a new stream with improved learnability. FEILDS employs a concept formation system for organizing its input stream into a concept (cluster) hierarchy. The system uses the concept and cluster hierarchy to identify the instance's concept and unlabeled data relevant to a concept. It also adopts the persistence assumption in temporal reasoning for inferring the relevance of concepts. Empirical evaluation indicates that FEILDS is able to improve the performance of existing learners particularly when learning from a stream with a few labeled data. Lastly, a new concept formation algorithm, one of the key components in the FEILDS architecture, is presented. The main idea is to discover intrinsic hierarchical structures regardless of the class distribution and the shape of the input stream. Experimental evaluation shows that the algorithm is relatively robust to input ordering, consistently producing a hierarchy structure of high quality

An Update Algorithm for Restricted Random Walk Clusters

This book presents the dynamic extension of the Restricted Random Walk Cluster Algorithm by Schöll and Schöll-Paschinger. The dynamic variant allows to quickly integrate changes in the underlying object set or the similarity matrix into the clusters; the results are indistinguishable from the renewed execution of the original algorithm on the updated data set