Editing Cluster analysis (section)

===[[Computer science]]===
; [[Software evolution]]
:Clustering is useful in software evolution as it helps to reduce legacy properties in code by reforming functionality that has become dispersed. It is a form of restructuring and hence is a way of direct preventative maintenance.
; [[Image segmentation]]
:Image segmentation is the process of dividing a digital image into multiple meaningful regions or segments to simplify and/or change the representation of an image, making it easier to analyze. These segments may correspond to different objects, parts of objects, or background areas. The goal is to assign a label to every pixel in the image so that the pixels with similar attributes are grouped together.
:This process is used in fields like medical imaging, computer vision, satellite imaging, and in daily applications like face detection and photo editing.
:[[File:Aurora_borealis_over_Eielson_Air_Force_Base,_Alaska.jpg|alt=The aurora borealis, or northern lights, above Bear Lake, Alaska|thumb|300x300px|The aurora borealis, or northern lights, above Bear Lake, Alaska]][[File:Polarlicht_2_kmeans_16_large.png|alt=Polarlicht 2 kmeans 16 large|thumb|300x300px|Image after running k-means clustering with ''k = 16''.]]'''Clustering in Image Segmentation:'''
:Clustering plays a significant role in image segmentation. It groups pixels into clusters based on similarity without needing labeled data. These clusters then define segments within the image.
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:Here are the most commonly used clustering algorithms for image segmentation:
:# '''[[K-means clustering|''K''-means Clustering]]:''' One of the most popular and straightforward methods. Pixels are treated as data points in a feature space (usually defined by color or intensity) and grouped into ''k'' clusters. Each pixel is assigned to the nearest cluster center, and the centers are updated iteratively.
:# '''[[Mean shift|Mean Shift Clustering]]:''' A non-parametric method that does not require specifying the number of clusters in advance. It identifies clusters by locating dense areas of data points in the feature space.
:# '''[[Fuzzy clustering|Fuzzy ''C''-means]]:''' Unlike ''k''-means, which assigns pixels to exactly one cluster, fuzzy ''c''-means allows each pixel to belong to multiple clusters with varying degrees of membership. 
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; [[Evolutionary algorithms]]
:Clustering may be used to identify different niches within the population of an evolutionary algorithm so that reproductive opportunity can be distributed more evenly amongst the evolving species or subspecies.
; [[Recommender systems]]
: Recommender systems suggest items, products, or other users to an individual based on their past behavior and current preferences. These systems will occasionally use clustering algorithms to predict a user's unknown preferences by analyzing the preferences and activities of other users within the same cluster. Cluster analysis is not the only approach for recommendation systems, for example there are systems that leverage graph theory. Recommendation algorithms that utilize cluster analysis often fall into one of the three main categories: Collaborative filtering, Content-Based filtering, and a hybrid of the collaborative and content-based.
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:'''Collaborative Filtering Recommendation Algorithm'''
: Collaborative filtering works by analyzing large amounts of data on user behavior, preferences, and activities to predict what a user might like based on similarities with others. It detects patterns in how users rate items and groups similar users or items into distinct “neighborhoods.” Recommendations are then generated by leveraging the ratings of content from others within the same neighborhood. The algorithm can focus on either user-based or item-based grouping depending on the context.<ref name="ReviewCluster">{{cite arXiv |eprint=2109.12839 |last1=Beregovskaya |first1=Irina |last2=Koroteev |first2=Mikhail |title=Review of Clustering-Based Recommender Systems |date=2021 |class=cs.IR }}</ref>
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[[File:FlowDiagram for Recommendation Systems.png|thumb|Flow diagram that shows a basic and generic approach to recommendation systems and how they utilize clustering.]]
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:'''Content-Based Filtering Recommendation Algorithm'''
: Content-based filtering uses item descriptions and a user's preference profile to recommend items with similar characteristics to those the user previously liked. It evaluates the distance between feature vectors of item clusters, or “neighborhoods.” The user's past interactions are represented as a weighted feature vector, which is compared to these clusters. Recommendations are generated by identifying the cluster evaluated be the closest in distance with the user's preferences.<ref name="ReviewCluster" />
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:'''Hybrid Recommendation Algorithms'''
: Hybrid recommendation algorithms combine collaborative and content-based filtering to better meet the requirements of specific use cases. In certain cases this approach leads to more effective recommendations. Common strategies include: (1) running collaborative and content-based filtering separately and combining the results, (2) adding onto one approach with specific features of the other, and (3) integrating both hybrid methods into one model.<ref name="ReviewCluster" />

; [[Markov chain Monte Carlo|Markov chain Monte Carlo methods]]
:Clustering is often utilized to locate and characterize extrema in the target distribution.
; [[Anomaly detection]]
:Anomalies/outliers are typically – be it explicitly or implicitly – defined with respect to clustering structure in data.
; [[Natural language processing]]
:Clustering can be used to resolve [[lexical ambiguity]].<ref name="mitpressjournals.org"/>
; [[DevOps]]
:Clustering has been used to analyse the effectiveness of DevOps teams.<ref name="stateofdevopsreport">{{cite report |url=https://services.google.com/fh/files/misc/2022_state_of_devops_report.pdf|title=2022 Accelerate State of DevOps Report|publisher=Google Cloud's DevOps Research and Assessment (DORA)|date=29 September 2022|pages=8, 14, 74 }}</ref>