an efficient algorithm Inc-Cluster to incrementally update the random walk distances given the edge weight increments. Complexity analysis is provided to estimate how much runtime cost Inc-Cluster can save. Experimental results demonstrate that Inc-Cluster achieves significant speedup over SA-Cluster on large

incremental algorithm for the single-source shortest-path problem with positive edge lengths. The aspect of our work that distinguishes it from other work on the dynamic shortest-path problem is its ability to handle "multiple heterogeneous modifications": between updates, the input graph is allowed

. The Galois (or concept) lattice produced from a binary relation has been proved useful for many applications. Building the Galois lattice can be considered as a conceptual clustering method since it results in a concept hierarchy. This article presents incremental algorithms for updating

In this paper we introduce an incremental clustering algorithm for undirected graphs. The algorithm can maintain clusters efficiently in presence of insertion and deletion (updation) of edges and vertices. The algorithm produces clusters that satisfies the quality requirement, given

the data and to restructure (or provide structure to) it. To achieve fast query response time, these views are often materialized. This paper studies incremental maintenance techniques for materialized views over semistructured data. We use the graph-based data model OEM and the query language Lorel

programming problem. We give an incremental algorithm based on gradient projection for efficiently solving this problem. The algorithm is considerably faster than using generic constraint optimization techniques and is comparable in speed to unconstrained stress majorization. We demonstrate the utility of our

. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same

thestudyofbiological networkstotheanalysis ofnetworksof scientific references through the exploration of communications networks such as the World Wide Web, it is the main purpose of this paper to introduce a novel, computationally efficient, approach to graph clustering in the evolutionary context. Namely, the method

Index Graph, which allows for incremental construction of a phrase-based index of the document set with an emphasis on efficiency, rather than relying on single-term indexes only. It provides efficient phrase matching that is used to judge the similarity between documents. The model is flexible

-ily used for incremental clustering, and is very expensive for frequent re-clustering over dynamically changing stream data. In this paper, we present an efficient incremental sub-space clustering algorithm for multiple streams over sliding windows. Our algorithm detects all the δ-CC-Clusters, which