Clustering is a sort of pre-writing that allows a writer to explore many ideas at the same time. Clustering, like brainstorming or free associating, allows a writer to start without any specific ideas. Write fast, circling and grouping words around the focal word. Then go back and look at those clusters. See which ones make sense together and which ones might want to be separated out into their own paragraphs or eliminated entirely.
The basic idea behind clustering is that you can get some good distance from your initial idea if you write about everything that comes to mind. Then you can return to these pieces of writing later with more focused ideas or parts of ideas. This helps prevent you from getting stuck in a rut when writing.
There are several different clustering techniques. You can group words by their similarity (such as using synonyms or antonyms), or you can use differences such as opposites or variations of a theme. You can also combine methods. For example, you could group words by meaning and then move on to the next group while waiting for new ideas to come to mind. The important thing is that you keep writing down new ideas as they come to you.
Once you've done some preliminary clustering, it's time to go back and read what you've written. Try to find connections between words in the same cluster.
Clustering is a method of brainstorming. It stresses the connections between concepts. Clustering is often referred to as webbing or mapping. In fact, clustering is the foundation of all visual brainstorming techniques. The term "cluster" comes from the fact that ideas are grouped into clusters based on similarity. For example, if you were trying to come up with new products for a company, you might cluster similar ideas together under one label (e.g., clothing) or group them by category (e.g., shirts-pants-scarves).
Clustering can be done manually by creating labels or categories to group ideas, or it can be done using computer software. Manual clustering requires that you understand the range of ideas being considered and then group them accordingly. This process can be difficult if you have many different ideas to consider. Thus, manual clustering is not recommended unless you have enough time to do so.
Computer-assisted clustering uses statistics and algorithms to identify groups of related ideas without strict supervision from the user. These groups are called clusters. Cluster analysis is a widely used tool in marketing research to find patterns in consumer behavior. A marketer might use this technique to find themes in online survey responses, for example, or to group products that show similar levels of popularity among buyers.
A cluster or map combines the two steps of brainstorming (noting and grouping ideas) into one. It also helps you to see at a glance the areas of the subject you have the most to speak about, which may assist you in deciding how to focus on a broad subject for writing. A good topic list should include topics that are both narrow and broad. Narrow topics can be used to paint a specific picture in your reader's mind, while broader topics allow you to cover a wider range of material.
There are two types of clusters: topical and conceptual. Topical clusters are based on the actual words/phrases that appear in the text, such as "crime scene photos," "police reports," and "witness statements." Conceptual clusters use the main ideas within the text to group related topics, such as "crimes against women" and "drugs and violence." While creating a topical cluster map, it is helpful to think about what questions people might ask about the crime scene, such as "Who was killed?" and "Where did it happen?". Remember to be concise when creating a topical cluster map; however, too many sub-topics under one main idea can be confusing for readers.
Conceptual clusters are more difficult to create because there are no clear boundaries between topics. However, by looking at the book as a whole and thinking about its main themes, you can begin to group related topics together.
Definition Text clustering is the automated classification of textual materials (for example, plain text documents, web pages, emails, and so on) into clusters based on their content similarity. The clusters can then be used for many different purposes such as information retrieval, document indexing, categorization, tagging, and so on.
Text clustering aims to group together texts that are "about the same thing". Given a set of texts, the task is to find groups of texts that are "about the same topic", where a topic can be defined in many ways, for example by an author, by a subject, or even simply by looking at how often certain words appear in the texts.
Texts within a cluster will usually share some common characteristics. For example, all the texts in a cluster may describe events that happened around the same time, come from the same source, relate to the same person or people, or otherwise be about the same topic. The texts may also have some similarities beyond what might be expected by chance. For example, they may use many of the same words in describing the same event, or they may include references to other texts which are also about the same topic.
Text clustering is useful because it can help with tasks such as information retrieval.
Word groupings or clusters are collections of words that share a common topic. The simplest approach to form a group is to collect synonyms for a certain term. The set of synonyms creates a cluster with one common meaning, and you have successfully learned several words for that one meaning. Clusters can also be based on the meanings of individual words. For example, the words hide, hidden, hiding all mean the same thing—that which is hidden. So, these three words are part of the same cluster.
Clusters can also include words that are used in conjunction with each other. For example, red, pink, rose refer to the same color. In general, if two or more words are needed to describe something exactly, they form a cluster.
A cluster dictionary defines clusters by listing examples of words that are used together. A cluster is said to have a shared subject or concept. For example, the cluster "animals" has dogs, cats, birds, and many other words that belong to this group. Animals is the shared subject of this cluster.
A cluster list is a compilation of clusters. Each entry in such a list will usually include a brief definition of the cluster, followed by a more detailed explanation of its meaning.