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List of street view services

Google Street View is the most comprehensive street view service in the world. It provides street view for more than 85 countries worldwide. The Mapillary project collects crowdsourced images from its users, which are licensed under a CC BY-SA li ...

Hitta.se

Hitta.se is a Swedish search engine that offers telephone directory, addresses and maps. The site was founded in June 2004. In February 2005, The Website Hitta.SES of the parent company Alta Teleadress information holding AB was bought I Hierta, ...

ImmersiVision

ImmersiVision Interactive Technologies Inc. is a Whistler, British Columbia, Canada based company, founded in early 2004, whose primarily revenue source was the production of immersive virtual reality. The product is similar to Google Streetview. ...

NORC (web service)

NORC was a street view website introduced in 2009 for Central and Eastern Europe. The site provided 360-degree panoramas from various cities and locations in Austria, Czech Republic, Hungary, Poland, Romania, Russia, and Slovakia. It is owned by ...

Affinity propagation

In statistics and data mining, affinity propagation is a clustering algorithm based on the concept of "message passing" between data points. Unlike clustering algorithms such as k -means or k -medoids, affinity propagation does not require the nu ...

BIRCH

BIRCH is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of BIRCH is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points ...

Canopy clustering algorithm

The canopy clustering algorithm is an unsupervised pre-clustering algorithm introduced by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. It is often used as preprocessing step for the K-means algorithm or the Hierarchical clustering algorit ...

Cluster-weighted modeling

In data mining, cluster-weighted modeling is an algorithm-based approach to non-linear prediction of outputs from inputs based on density estimation using a set of models that are each notionally appropriate in a sub-region of the input space. Th ...

CURE algorithm

CURE is an efficient data clustering algorithm for large databases. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.

Data stream clustering

In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data, financial transactions etc. Data stream clustering is usually studied as a streaming algorithm a ...

DBSCAN

Density-based spatial clustering of applications with noise is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jorg Sander and Xiaowei Xu in 1996. It is a density-based clustering non-parametric algorithm: given a set of ...

FLAME clustering

Fuzzy clustering by Local Approximation of MEmberships is a data clustering algorithm that defines clusters in the dense parts of a dataset and performs cluster assignment solely based on the neighborhood relationships among objects. The key feat ...

Fuzzy clustering

Fuzzy clustering is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters such that the elements of the same cluster are as similar as possible ...

Hoshen–Kopelman algorithm

The Hoshen–Kopelman algorithm is a simple and efficient algorithm for labeling clusters on a grid, where the grid is a regular network of cells, with the cells being either occupied or unoccupied. This algorithm is based on a well-known union-fin ...

K q-flats

In data mining and machine learning, k {\displaystyle k} q {\displaystyle q} -flats algorithm is an iterative method which aims to partition m {\displaystyle m} observations into k {\displaystyle k} clusters where each cluster is close to a q {\d ...

K-means clustering

k -means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k -means clustering aims to partition n observations into k clusters in which each observation belongs ...

K-means++

In data mining, k -means++ is an algorithm for choosing the initial values for the k -means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k -means problem - a ...

K-medians clustering

In statistics and data mining, k -medians clustering is a cluster analysis algorithm. It is a variation of k -means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Th ...

K-medoids

The k -medoids or partitioning around medoids algorithm is a clustering algorithm reminiscent to the k -means algorithm. Both the k -means and k -medoids algorithms are partitional and both attempt to minimize the distance between points labeled ...

Low-energy adaptive clustering hierarchy

Low-energy adaptive clustering hierarchy is a TDMA-based MAC protocol which is integrated with clustering and a simple routing protocol in wireless sensor networks. The goal of LEACH is to lower the energy consumption required to create and maint ...

Nearest-neighbor chain algorithm

In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical clustering. These are methods that take a collection of points as input, and create a hierarc ...

OPTICS algorithm

Ordering points to identify the clustering structure is an algorithm for finding density-based clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jorg Sander. Its basic idea is similar to DBSCA ...

Pitman–Yor process

In probability theory, a Pitman–Yor process denoted PY, is a stochastic process whose sample path is a probability distribution. A random sample from this process is an infinite discrete probability distribution, consisting of an infinite set of ...

Quantum clustering

Quantum clustering, is a data clustering algorithm accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a sigma value, a hyper-parameter which can be manually defined and manipulated to suit the ...

Single-linkage clustering

In statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion, at each step combining two clusters that contain the closest pair of elements not yet belonging ...

SUBCLU

SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kroger. It is a subspace clustering algorithm that builds on the density-based clustering algorithm DBSCAN. SUBCLU can find clusters in axis ...

Wards method

In statistics, Wards method is a criterion applied in hierarchical cluster analysis. Wards minimum variance method is a special case of the objective function approach originally presented by Joe H. Ward, Jr. Ward suggested a general agglomerativ ...

Balanced clustering

Balanced clustering is a special case of clustering where, in the strictest sense, cluster sizes are constrained to ⌊ n k ⌋ {\displaystyle \lfloor {n \over k}\rfloor } or ⌈ n k ⌉ {\displaystyle \lceil {n \over k}\rceil }, where n {\displaystyle n ...

Dasguptas objective

In the study of hierarchical clustering, Dasguptas objective is a measure of the quality of a clustering, defined from a similarity measure on the elements to be clustered. It is named after Sanjoy Dasgupta, who formulated it in 2016. Its key pro ...

Davies–Bouldin index

The Davies–Bouldin index is a metric for evaluating clustering algorithms. This is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset. This ha ...

Dunn index

The Dunn index is a metric for evaluating clustering algorithms. This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index, in that it is an internal evaluation scheme, where the result is based on the clu ...

Fowlkes–Mallows index

Fowlkes–Mallows index is an external evaluation method that is used to determine the similarity between two clusterings. This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classificatio ...

Hopkins statistic

The Hopkins statistic is a way of measuring the cluster tendency of a data set. It belongs to the family of sparse sampling tests. It acts as a statistical hypothesis test where the null hypothesis is that the data is generated by a Poisson point ...

Rand index

The Rand index or Rand measure in statistics, and in particular in data clustering, is a measure of the similarity between two data clusterings. A form of the Rand index may be defined that is adjusted for the chance grouping of elements, this is ...

Silhouette (clustering)

Silhouette refers to a method of interpretation and validation of consistency within clusters of data. The technique provides a succinct graphical representation of how well each object has been classified. The silhouette value is a measure of ho ...

Binomial process

Let P {\displaystyle P} be a probability distribution and n {\displaystyle n} be a fixed natural number. Let X 1, X 2, …, X n {\displaystyle X_{1},X_{2},\dots,X_{n}} be i.i.d. random variables with distribution P {\displaystyle P}, so X i ∼ P {\d ...

Determinantal point process

In mathematics, a determinantal point process is a stochastic point process, the probability distribution of which is characterized as a determinant of some function. Such processes arise as important tools in random matrix theory, combinatorics, ...

Index of dispersion

In probability theory and statistics, the index of dispersion, dispersion index, coefficient of dispersion, relative variance, or variance-to-mean ratio, like the coefficient of variation, is a normalized measure of the dispersion of a probabilit ...

Mixed binomial process

A mixed binomial process is a special point process in probability theory. They naturally arise from restrictions of Poisson processes bounded intervals.

Nu-transform

In the theory of stochastic processes, a ν-transform is an operation that transforms a measure or a point process into a different point process. Intuitively the ν-transform randomly relocates the points of the point process, with the type of rel ...

Simple point process

Let S {\displaystyle S} be lcscH and let S {\displaystyle {\mathcal {S}}} be the σ {\displaystyle \sigma } -algebra consisting of all relatively compact subsets of S {\displaystyle S}. A point process ξ {\displaystyle \xi }, interpreted as random ...

Toponymy of England

The toponymy of England, like the English language itself, derives from various linguistic origins. Modern interpretations are apt to be inexact: many English toponyms have been corrupted and broken down over the years, due to changes in language ...

Regis (place)

Regis, Latin for "of the king", occurs in numerous English place names. The name usually recalls the historical ownership of lands or manors by the Crown. In other places it honours royal associations rather than ownership. The "Regis" form was o ...

Utqiagvik, Alaska

Utqiagvik, officially the City of Utqiagvik, formerly known as Barrow, is the largest city and the borough seat of the North Slope Borough in the U.S. state of Alaska and is located north of the Arctic Circle. It is one of the northernmost public ...

Georgetown street renaming

The Georgetown street renaming occurred as a result of an 1895 act of the United States Congress that ended even the nominal independence of Georgetown from Washington, D.C. The Act required, inter alia, that the street names in Georgetown be cha ...

Almberg

The 1.139-metre-high Almberg lies about 20 kilometres northeast of Freyung near the border between Germany and Czechia. Its mountainsides are mainly used for skiing. On its eastern side is Mitterfirmiansreut, a village whose main focus is winter ...

Breitenauriegel

The Breitenauriegel is a mountain in the Bavarian Forest of Germany. Mountain 1.116 m above sea level, high NHN and rises from the crest of the Vorderer Bavarian forest near Bischofsmais and neighboring peaks GeiSkopf and Dreitannenriegel. On top ...

Brotjacklriegel

Seen from the River Danube, the Brotjacklriegel is the first high mountain in the Bavarian Forest. It is 1.011 m above NHN and lies in the county of Freyung-Grafenau in the German federal state of Bavaria. It is a symbol of the Sonnenwald region ...

Dreisesselberg (Bavarian Forest)

The Dreisesselberg is located in the eastern part of Lower Bavaria in the county of Freyung-Grafenau. It rises southeast of the village of Haidmuhle and northeast of the village of Neureichenau. The Czech border is 370 metres South-East from the ...

Dreitannenriegel

The Dreitannenriegel is a mountain, 1.090.2 m above sea level, in the Bavarian Forest. In the mountain rises from the top of the front Bavarian forest high above the Lower Bavarian district of Deggendorf city and lies in the municipality of Grafl ...

Encyclopedic dictionary

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