Hierarchical Clustering: A Survey

Shetty, Pranav; Singh, S.R.K.

doi:10.22271/allresearch.2021.v7.i4c.8484

Cited by 26 publications

(12 citation statements)

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“…Because it can be calculated from the Cartesian coordinate system using the Pythagorean theorem, this distance is frequently referred to as the Pythagorean distance. [25].…”

Section: Study Areamentioning

confidence: 99%

Time of concentration estimated of overland flow

Pramana,

Harisuseno

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Time of concentration (Tc) is a crucial aspect in the hydrological model, especially for determining flood discharge. Time concentration is the amount of time required for water to go from a watershed’s farthest point to its outflow. Although it is an important value, however, Tc does not have a universal equation that can be used as a reference. Based on the literature review, there are several concentration time calculation methods. Each method has its parameters and approach to produce varied values. Several experimental methods were applied to calculate concentration time at sites, among others are Kirpich, SCS Lag, and FAA. The calculation results are displayed in a dendrogram with group division by using the Euclidean approach. The calculation results show that the difference in Tc values can reach up to 500%, hence Grimaldi calls it a paradox in modern hydrology.

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“…Because it can be calculated from the Cartesian coordinate system using the Pythagorean theorem, this distance is frequently referred to as the Pythagorean distance. [25].…”

Section: Study Areamentioning

confidence: 99%

Time of concentration estimated of overland flow

Pramana,

Harisuseno

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…In the context of document grouping, Bisecting K-Means (BKMS) is a divisional hierarchical clustering algorithm [6]. K-means is a division algorithm that consistently selects the partition with the greatest overall similarity, determined by the pairwise similarity of all the points in a cluster [13].…”

Section: Bisecting K-means (Bkms)mentioning

confidence: 99%

“…Clustering can also be defined as grouping data into classes or clusters so that objects in collections have on [7,8]. Data is a collection of facts, images, or behavior that will be studied manually so that it can predict things that will happen in the future [6]. Data Mining is very important and needs to be done especially for processing extensive data, facilitating the activity of recording a transaction, pattern, or behavior, and for data warehousing processes to provide accurate information for users.…”

Section: Introductionmentioning

confidence: 99%

Hierarchy Clustering Implementation on YouTube's Top Data

Ray¹,

Nandkeolyar²,

Santosa³

et al. 2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and

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Clustering is a method or process of grouping datasets into various clusters to produce variations in smaller clusters. Clustering has broad application fields such as data concept construction, pattern recognition, web search, simplification, security, and several other areas. Clustering methods are classified into two types, hierarchies and partitions. The hierarchical clustering method defines the cluster hierarchy by separating and combining them, whereas the partitioning method involves defining and evaluating sections based on criteria. Thus, the selected clustering algorithm must be efficient. This article focuses on clustering algorithms for obtaining and processing YouTube Channel Top Data.

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“…In this work, we propose a novel assessing metric of reverse engineering on RL rewards, and develop a metric via normalized mutual information of reward clusters (C-NMI). We employ an agglomerative nesting algorithm (AGNES) [52] for dynamical C-NMI computing to quantify the reward clusters' similarity compared with the reward ground truth. We build a 4-order tensor model embedded with manipulated trajectories, which are formed from both suboptimal [43] and inverted [42] trajectories.…”

Section: Introductionmentioning

confidence: 99%

A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

Chen

Liu

Baker

et al. 2023

IEEE Trans. Artif. Intell.

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Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference and the mean reward loss, which we call them EVD and MRL respectively. Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this paper, based on the metric called normalized mutual information of reward clusters (C-NMI) we propose a novel IRL assessment; we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a 4-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in Object World, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59% respectively when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations.Impact Statement-In this work, we pay attention to the inconformity problem of MRL-and EVD-based IRL assessment. There are two main challenges for us to address: (1) how to design a novel metric by combining the advantages of both MRL and EVD, and (2) how to construct a comprehensive assessment method for accurate comparison and analysis. To address such challenges, we craft a novel assessment of IRL based on the metric called normalized mutual information of reward clusters (C-NMI). Hence we attempt to fill the existing research gap by considering a middle-granularity state space between the entire state space and the certain sampled space. We list all of the notation and parameters used in the rest of this paper in Table I.

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Hierarchical Clustering: A Survey

Cited by 26 publications

References 0 publications

Time of concentration estimated of overland flow

Time of concentration estimated of overland flow

Hierarchy Clustering Implementation on YouTube's Top Data

A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

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