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The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jun 30, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Fast approximate hierarchical clustering using similarity heuristics.

Meelis Kull1, Jaak Vilo

  • 1Institute of Computer Science, University of Tartu, Liivi 2, 50409 Tartu, Estonia. Meelis.Kull@ut.ee

Biodata Mining
|September 30, 2008
PubMed
Summary

HappieClust offers an approximate agglomerative hierarchical clustering (AHC) method that significantly speeds up analysis of large biological datasets. This approach achieves biologically meaningful clustering efficiently, overcoming computational challenges of standard AHC.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jun 30, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Analysis

Background:

  • Agglomerative hierarchical clustering (AHC) is a widely used unsupervised learning method in biological data analysis.
  • Standard AHC algorithms face scalability issues due to their quadratic complexity in computing pairwise distances for large datasets.

Purpose of the Study:

  • To develop an approximate AHC algorithm, HappieClust, that accelerates the analysis of large biological datasets.
  • To provide a computationally efficient alternative to standard AHC without compromising biological relevance.

Main Methods:

  • HappieClust employs an approximate AHC approach by calculating only a subset of pairwise distances.
  • A similarity heuristic using pivot objects is utilized to efficiently identify similar object pairs, mimicking full AHC greedy choices.
  • Clustering quality is assessed using global clustering metrics, biological function enrichment in subtrees, and subtree content conservation.

Main Results:

  • HappieClust achieves biologically meaningful clustering for large datasets over ten times faster than full AHC algorithms.
  • The approximate method demonstrates high quality comparable to standard AHC, validated by multiple assessment measures.
  • The algorithm effectively identifies similar objects, enabling efficient and accurate hierarchical clustering.

Conclusions:

  • HappieClust is suitable for large-scale gene expression visualization and analysis.
  • The software is accessible for use on personal computers and online web applications.
  • This method provides a scalable solution for complex biological data analysis challenges.