# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Clustering Algorithms
#### 3.5.1 Hierarchical Algorithms  
Hierarchical algorithms create a hierarchy of clusters. Hierarchical clustering algorithms have become the standard method for document clustering [150] by combining the ideal measure similarities such as cosine similarity, Jaccard similarity coefficient and Dice coefficient. The most popular text clustering technique that produces nested groups in the form of a hierarchy is called hierarchical clustering. To use this strategy, the category must be hierarchical. Generally, the relevant objects will be updated if the category changes. The output of using a hierarchical clustering method is a single-category tree. A sample of hierarchical clustering is shown in Figure 15; each class node has several child nodes, and a brother node is a division of its parent nodes. This can form extended, almost identical clusters. For clusters of comparable sizes, the complete-link approach is preferable (in volume). The similarity between two groups can be defined as the degree to which their two most similar objects and most distinct [150; 151]. As a result, this method allows for data classification at various granularities. In general, hierarchical clustering is accurate. However, each class must integrate and compare the overall similarity of all classes to choose the two more similar classes, which is  
Figure 15: Sample of hierarchical clustering. comparably slow. Another problem of hierarchical clustering is that once a stage merge or split is finished, it cannot be halted, making it impossible to correct a mistake [147]. The hierarchical clustering techniques may be classified into two groups based on the formation of the category tree methods: the top-down split technique and the bottom-up integration technique [151]. Bottom-up (merge-up) hierarchical clustering starts with a single item. It begins with an item as a solitary category and then consistently combines two or more appropriate categories. The hierarchical clustering does not loop as long as the stop criteria are fulfilled (the number of parameters is generally K, where K = Number of clusters). The bottom-up hierarchical clustering method is viewed as constructing the tree, consisting of data on the class hierarchy and the degree of similarity between all classes. Hierarchical clustering has the following advantages: it may be used with any shape, degree of similarity or distance and it features an inherently adaptable clustering granularity. One drawback of hierarchical clustering is the ambiguous termination condition: once the clustering is complete, it should define the human experience. Often, this technique cannot be rebuilt to provide better results, and the faults produced cannot be corrected [147; 151]. The top-down (split-down) hierarchical clustering technique begins with a single completed item and splits it into multiple categories. The standard method is to construct a minimal spanning tree on related graphs, and then, at each step, choose a side closest to (or farthest from) the spanning tree in terms of similarity and eliminate it. It can create a new category if one side is removed. The cluster may cease whenever the lowest similarity reaches a certain threshold. The top-down technique often involves more computing than the bottom-up method, making top-down method applications less common than the latter. A cluster in the top-down approach is split into two categories simultaneously, and this process continues until the class is broken into (k) clusters. Generally, both hierarchical clustering approaches are simple and adaptable enough to tackle multi-granularity clustering issues. They can handle a wide variety of attributes and can employ many kinds of distance or similarity measurements. The bottom-up and top-down hierarchical clustering approaches have these limitations: determining the algorithm's termination criteria and choosing the merge or split points are challenging.