Agglomerative Hierarchical Clustering (AHC)

Agglomerative Hierarchical Clustering (AHC) is part of:
  • Pro Core statistical software

  • System configuration

    • Windows:
      • Versions: 9x/Me/NT/2000/XP/Vista/Win 7/Win 8
      • Excel: 97 and later
      • Processor: 32 or 64 bits
      • Hard disk: 150 Mb
    • Mac OS X:
      • OS: OS X
      • Excel: X, 2004 and 2011
      • Hard disk: 150Mb.

Benefits

  • Easy and user-friendly
    Easy and user-friendly XLSTAT is flawlessly integrated with Microsoft Excel which is the most popular spreadsheet worldwide. This integration makes it one of the simplest available tools to work with as it utilizes the same philosophy as Microsoft Excel. The program is accessible in a dedicated XLSTAT tab. The analyses are grouped into functional menus. The dialog boxes are user-friendly and setting up an analysis is straightforward.
  • Data and results shared seamlessly
    Data and results shared seamlessly One of the greatest advantages of XLSTAT is the way you can share data and results seamlessly. As the results are stored in Microsoft Excel, anyone can access them. There is no need for the receiver to have an XLSTAT license or any additional viewer which makes your team-work easier and more affordable. In addition, results are easily integrable into other Microsoft Office software such as PowerPoint, so that you can create striking presentation in minutes.
  • Modular
    Modular XLSTAT is a modular product. XLSTAT-Pro is a core statistical module of XLSTAT which includes all the mainstream functionalities in statistics and multivariate analysis. More advanced features contained in add-on modules can be added for specific applications. This way you can adapt the software to your needs making the software more cost-efficient.
  • Didactic
    Didactic The results of XLSTAT are organized by analysis and are easy to navigate. Moreover useful information is provided along with the results to assist you in your interpretation.
  • Affordable
    Affordable XLSTAT is a complete and modular analytical solution that can suit any analytical business needs. It is very reasonably priced so that the return of your investment is almost immediate. Any XLSTAT license comes with top level support and assistance.
  • Accessible - Available in many languages
    Accessible - Available in many languages We have ensured XLSTAT is accessible to everyone by making the program available in many languages, including Chinese, English, French, German, Italian, Japanese, Polish, Portuguese and Spanish.
  • Automatable and customizable
    Automatable and customizable Most of the statistical functions available in XLSTAT can be called directly from the Visual Basic window of Microsoft Excel. They can be modified and integrated to more code to fit to the specificity of your domain. Adding tables and plots as well as modifying existing outputs becomes easy. Furthermore, XLSTAT includes some special tools on the dialog boxes to generate automatically the VBA code in order to reproduce your analysis using the VBA editor or to simply load pre-set settings. This effortless automation of routine analysis will be a huge time saver on your part.

Advantages of Agglomerative Hierarchical Clustering

Agglomerative Hierarchical Clustering (AHC) is a classification method which has the following advantages:

Principle of Agglomerative Hierarchical Clustering

Agglomerative Hierarchical Clustering (AHC) is an iterative classification method whose principle is simple.

  1. The process starts by calculating the dissimilarity between the N objects.
  2. Then two objects which when clustered together minimize a given agglomeration criterion, are clustered together thus creating a class comprising these two objects.
  3. Then the dissimilarity between this class and the N-2 other objects is calculated using the agglomeration criterion. The two objects or classes of objects whose clustering together minimizes the agglomeration criterion are then clustered together.

This process continues until all the objects have been clustered.

These successive clustering operations produce a binary clustering tree (dendrogram), whose root is the class that contains all the observations. This dendrogram represents a hierarchy of partitions. It is then possible to choose a partition by truncating the tree at a given level, the level depending upon either user-defined constraints (the user knows how many classes are to be obtained) or more objective criteria.

Agglomerative Hierarchical Clustering aggregation methods

XLSTAT proposes several aggregation methods:

Proximities used in Agglomerative Hierarchical Clustering

The proximity between two objects is measured by measuring at what point they are similar (similarity) or dissimilar (dissimilarity). If the user chooses a similarity, XLSTAT converts it into a dissimilarity as the AHC algorithm uses dissimilarities. The conversion for each object pair consists in taking the maximum similarity for all pairs and subtracting from this the similarity of the pair in question.

XLSTAT proposes several similarities/dissimilarities that are suitable for a particular type of data:

  Similarity Dissimilarity
Quantitative data Pearson's coefficient of correlation Spearman's coefficient of rank correlation Kendall's coefficient of rank correlation Inertia Covariance (n) Covariance (n-1) Percent agreement Euclidean distance Chi-square distance Manhattan distance Pearson's dissimilarity Spearman's dissimilarity Kendall's dissimilarity Percent disagreement
Binary data (0/1) Jaccards coefficient Dice coefficient Sokal & Sneath coefficient (2) Rogers & Tanimoto coefficient Simple matching coefficient Indice de Sokal & Sneath coefficient (1) Phi coefficient Ochiais coefficient Kulczinskis coefficient Percent agreement Jaccards coefficient Dice coefficient Sokal & Sneath coefficient (2) Rogers & Tanimoto coefficient Simple matching coefficient Indice de Sokal & Sneath coefficient (1) Phi coefficient Ochiais coefficient Kulczinskis coefficient Percent agreement

Note: For non-binary categorical variables, it is preferable to first perform a Multiple Correspondence Analysis (MCA) and to consider the coordinates of the observations on the factorial axes as new variables.

Results provided by XLSTAT for Agglomerative Hierarchical Clustering

Tutorials

Screenshots