Multi-level Hierarchical Clustering Research Articles

Recognition and Classification of Homogeneous Landscape With Visitor–Employed Photography and Cloud Image Annotation API—An Example of the Riverscape in Nihonbashi, Tokyo, Japan

Effective classification of landscape photographs is a vital step in data processing and environment analysis. With the popularity of crowdsourcing geo-information, an increasing number of studies have used geotagged photographs to visualize how people perceive and interact with destinations and explore the aesthetic, cultural, and recreational value of the areas. In recent years, machine-learning algorithms for image recognition have dramatically improved the efficiency of the assignment of keywords and provide possibilities for the automatic classification of numerous photographs. However, the applicability of such methods for the practical landscape classification is still not clear, especially for the photographs presenting a homogeneous landscape that has similar characteristics. This study developed a semi-automatic classifier for homogeneous landscape photographs by using Google Cloud Vision API and multi-level hierarchical clustering. The classifier was applied to the classification of urban riverscape photographs, which is a typical example of homogeneous landscapes in Nihonbashi, Tokyo, Japan. The riverscapes can be classified into 9 characteristic groups by the classifier and the visual impression of these groups matches well with our intuitive feeling. A confusion matrix showed that the overall accuracy was 82.61%, indicating a strong agreement between the classifier and manual classification. Therefore, the classifier is practical for classifying homogeneous riverscape photographs. Such methodology also provides the possibility of public participation in the assessing process, which, in turn, contributes to urban tourism management.

Energy efficient cooperative CRN spectrum sharing using multi-level hierarchical clustering with MCDM

Energy efficient cooperative CRN spectrum sharing using multi-level hierarchical clustering with MCDM

Energy efficient cooperative CRN spectrum sharing using multi-level hierarchical clustering with MCDM

Spectrum sensing is playing a vital role in cognitive radio networks (CRN). In cooperative sensing model, energy, task assignment and free channel allocation for requested CRs mostly varies based on location of CR from cluster head. Since heterogeneous CR users are participating in the cooperation, their willingness, battery power and sensing accuracy are to be considered for CR's weight calculation. Otherwise overall sensing results trustworthiness and performance of sensing task may be degraded. Using multi criteria decision making (MCDM) scheme we have calculated each node weight and given that as input to multi-level hierarchical clustering technique. Based on the traffic of each blocks, sensing task will be allocated to a cluster. In MCDM, we derived the weight by using combination of modified analytical hierarchical process (MAHP) for giving weight for attributes of each CR and VIKOR. Final result shows that increased average lifetime of each participating nodes in the simulation.

Tolerance Values and Effects of Selected Environmental Determinants on Diatom Distribution in Northwest and North Central Washington, USA

Abstract. Diatoms were collected at 165 stream, river, wall seep, and pond/lake sites in 9 counties in northwest and north central Washington, including the Olympic Pennsisula, over a 5-year period. Four hundred and fifteen species in 77 genera were identified. Species assemblages were compared to altitude, physicochemical factors, aquatic habitats, and land use on west and east sides of the North Cascade Range. Species richness averaged 30.5 per site on the west side and 43 on the east side. Diatom species richness showed significant positive correlations to total nitrogen, total phosphorus, pH, specific conductance, channel embeddedness, and water temperature, and a significant negative correlation to percent canopy cover. A multilevel hierarchical clustering model separated streams, rivers, wall seeps, and ponds/lakes into geographic and land-use regions based on diatom assemblages. A multimetric index (diatom tolerance index [DTI]) was developed to determine environmental tolerance levels for diatom s...

IMAGE STRUCTURE ANALYSIS BY 3-STAGES CLUSTERING

An approach for decomposition of visual images by clustering and breaking them down into geometric figures is considered. Multilevel hierarchical clustering algorithm to form three emphasized levels of clusters such as rectangles, closed regions and integrated areas is proposed. Advantages of such decomposition in three stages are as follows: images covered by rectangles are planned to be formatted and compressed, image fragments could be taken for the preliminary pattern recognition or could easily be corrected, hierarchically constructed fragments are good material to form pattern features for searching procedures. The algorithm complexity, the proposed approach of scanning searching area to reduce it, the rolling up criteria and key parameters for its control are investigated. The results of pattern analysis by structure features for some practical problems are presented in the article.

Tolerance Values and Effects of Selected Environmental Determinants on Caddisfly (Trichoptera) Distribution in Northwest and North Central Washington, USA

Caddisflies were collected at 181 wall seep, stream, river, and lake habitats in 7 counties in northwest and north central Washington over a 6-year period. From 17,405 specimens, we identified 164 adult caddisfly species within 62 genera and 16 families. Twenty taxa were new state records, bringing the number of species currently reported from Washington to 230. Species assemblages were compared to altitude, physicochemical factors, aquatic-habitats, and land use (urban, agriculture, and forest) on the west and east sides of the North Cascade Range. Species richness showed significant positive correlations to altitude and pH and showed significant negative correlations to total phosphorus, total nitrogen, and specific conductivity, as well as especially to channel embeddedness. A multilevel hierarchical clustering model separated wall seeps, streams, and rivers into geographic and land-use regions based on adult caddisfly assemblages. We used a multimetric index (caddisfly tolerance index [CTI]) to determine environmental tolerance levels for adult caddisfly species. The index performed well in distinguishing among the effects of total phosphorus, total nitrogen, specific conductance, and channel embeddedness on the distribution of caddisfly species. These CTI values provide baseline information for monitoring changes in ecosystem health in drainages throughout Washington landscapes.

Distributed iterative aggregation algorithms for box-constrained minimization problems and optimal routing in data networks

A gradient-projection algorithm using iterative aggregation and disaggregation is proposed and analyzed for box-constrained minimization problems. In a distributed computation model, the algorithm is shown to converge. The algorithm is applied to optimal routing in a large interconnected data communication network, resulting in a multilevel hierarchical clustering that naturally fits the hierarchical topological structure of such networks. An implementation of the algorithm for a 52-node network shows that the serial version of the algorithm has a saving of 35% of the computational time as compared to a path-formulated gradient-projection code that is among the fastest existing programs for path-formulated optimal routing. >