This paper introduces a hybrid hierarchical clustering method, which is a novel method for speeding up agglomerative hierarchical clustering by seeding the algorithm with clusters obtained from K-means clustering. This work describes a benchmark study comparing the performance of hybrid hierarchical clustering to that of conventional hierarchical clustering. The two clustering methods are compa...

Kebakaran hutan di Indonesia setiap tahunnya masih sering terjadi. Dalam menanggulangi kebakaran sendiri, para peneliti belakangan ini semakin fokus untuk melakukan pengembangan sistem yang mampu prediksi hutan. Selain mengenai hutan, antisipasi dapat dilakukan membantu menangani pencegahan salah satunya adalah pengelompokkan terhadap wilayah memiliki potensi kebakaran. Pada penelitian Clusteri...

knowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering  in which there is no need to  be peculiar about how to select initial values. due to properly converting the task of optimization to an equivalent...

Hierarchical clustering is an important technique for hierarchical data exploration applications. However, most existing hierarchial methods are based on traditional one-side clustering, which is not effective for handling high dimensional data. In this paper, we develop a partitional hierarchical co-clustering framework and propose a Hierarchical Information-Theoretical Co-Clustering (HITCC) a...

Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N...

In the MediaEval 2016 Retrieving Diverse Social Images Task, we proposed a general framework based on agglomerative hierarchical clustering (AHC). We tested the provided credibility descriptors as a vector input for our AHC. The results on devset showed that this vector based on the credibility descriptors is the best feature, but unfortunately that is not confirmed on testset. To merge several...

The paper considers techniques for grouping objects that are described with many quantitative and qualitative attributes and may exist in several copies. Such multi-attribute objects may be represented as multisets or sets with repeating elements. Multiset characteristics and operations under an arbitrary number of multisets are determined. The various options for the objects’ aggregation (addi...