uberpool means

The implementation of this clustering algorithm is the third assignment in the data mining course. The first two assignments were made using other people's software and seldom implemented by themselves, the first is to use sqlserver2008's business intelligence tool to create a data warehouse, data processing, and warehouse model, dimension tables, and fact tables, however, during the examination, we should also establish common data warehouse models. The second time we ran some provided data usi

category be identified before classification, and that each element is mapped to a category, and that clustering is an observational learning, which can be unaware of the category or even the number of categories before clustering, and is unsupervised learning. At present, clustering is widely used in statistics, biology, database technology and marketing and other fields, the corresponding algorithm is also very much. This paper only introduces one of the simplest clustering algorithm--k mean

Clustering Concepts:Clustering: The simple thing is to divide the similar things into a group. Different from classification (classification), classification should belong to supervised learning. In clustering, we don't care what a class is, and the goal we need to achieve is to get something similar together, so a clustering algorithm usually needs to know how to calculate the similarity to get started. Clustering does not require the use of training data for learning, should belong to unsuperv

(The final exam is coming, so the rough, please understand the reader.) )First, ConceptK-means is a prototype-based, partitioned clustering technique. It attempts to discover clusters (represented by centroid) of the user-specified number (K). The K-means algorithm accepts the input k, then divides the N data objects into K clusters to satisfy the obtained clusters: objects in the same cluster have higher s

K-means is a clustering algorithm:Here, we use K-means to classify 31 cities.The city's data is stored in the City.txt file, which reads as follows:bj,2959.19,730.79,749.41,513.34,467.87,1141.82,478.42,457.64tianjin,2459.77,495.47,697.33,302.87,284.19,735.97,570.84,305.08hebei,1495.63,515.90,362.37,285.32,272.95,540.58,364.91,188.63shanxi,1406.33,477.77,290.15,208.57,201.50,414.72,281.84,212.10nmg,1303.97,5

algorithm Process : Random selection of k seed points The distance from all points to the seed point, and the point into the nearest seed point group When all points are within the group, the seed points are moved to the seed center Repeat the 2, 3 procedure above until the seed point is not moved Advantages and Disadvantages Advantages: Easy to implement Disadvantage: May converge to local minimum, slow convergence on large-scale data Thinking:

Original: http://www.cnblogs.com/luxiaoxun/archive/2013/05/09/3069594.htmlClustering Chinese translation as "clustering", simply said to be similar to a group of things, with the classification (classification), for a classifier, usually need you to tell it "this thing is divided into XXX class" such as some examples, ideally, a Classifier will focus on "learning" from the training it receives, thus having the ability to classify unknown data, a process that provides training data, often called

K-means algorithm is a clustering algorithm, the cluster is of course unsupervised, given the initial data set $\left \{x_i \right\}_{i=1}^n$, K-means will divide the data into $K $ clusters, each cluster represents a different category,K-means algorithm as follows: 1. Select K centroid from training set $\left \{x_i \right\}_{i=1}^n$, respectively, $\l

Nine methods for success:1. Dare to make decisions-overcome the habit of hesitationThe biggest problem that many people do not accomplish anything is the lack of decision-making means. They always look around and think about it, and miss the best time to succeed. When the possibility of success is reached, those who make major decisions dare to take the lead.2. Challenge weakness-completely change your defectsEveryone has weaknesses. Those who do not

Before we implemented the K-means algorithm, we mentioned the flaw in itself: 1. May converge to local minimum value2. Slow convergence on large data sets At the end of the last blog post, when the local minimum is caught, the processing method is to run the K-means algorithm several times, then select the Distortion function J as the best clustering result. This is obviously not acceptable to

Brief introduction:This section describes the algorithms in the 12th and 13 episodes of the Stanford Machine learning public class: K-means algorithm, Gaussian mixture model (GMM). (9, 10, 11 episodes do not introduce, skip the ha)First, K-means algorithmIt belongs to unsupervised learning clustering algorithm, given a set of non-calibrated data (input sample), classify it, the hypothesis can be divided int

K-means algorithm MATLAB and opencv code, k-meansopencv In the previous blog, I wrote the k-means clustering algorithm and the improved k-means algorithm. This blog posts the corresponding MATLAB and C ++ code.The following is the MATLAB code to use k-means for segmentation: % Functions: how to Use Kmeans clustering t

Background Description:RGB encoding: For an image that directly uses 24bit for each pixel, each pixel uses an 8-bit unsigned integer (0-255) to denote red or green or blue.Compression Purpose:The 128x128 size picture is represented by the original 24bit-compressed into->16bit to represent the image of each pixel.Compression method:For each pixel, use K-means to select 16bits to represent the original 24bits. Of course, the original 24bits is expressed

1.k-means algorithm brief and code prototype One of the most important algorithms in data mining is K-means, which I do not introduce in detail here. If you are interested, you can take a Chenhao blog: Http://www.csdn.net/article/2012-07-03/2807073-k-means is a good speaker. In general, K-means cluste

The K-means method and ISODATA Method are two basic clustering methods. As the name suggests, K-means to specify K classes, and then get the last K centers through the initial center iteration. The initial center can be selected randomly or randomly, or the first K samples can be taken as the initial center. The final result of the cluster is closely related to the initial cluster center. Different initial

I small white, first published blog, Big God Detour, do not like to spray.Recently the company asked for some machine learning content, so in the reading of some machine learning related information, recently read the book name called Machine learning combat. This is a good book, well worth reading.Ok, don't talk nonsense, get into the subject of our today.K-Means algorithm (K-means algorithm)1.k-

DirectoryEM algorithm (1): K-means algorithmEM algorithm (2): GMM Training algorithmEM algorithm (3): The EM algorithm is detailed　　　　　　　　　　　　　　　　　　　　　　　　　　　EM algorithm (1): K-means algorithm1. IntroductionK-means algorithm is a kind of unsupervised clustering algorithm, the purpose is to divide the data without tags into several classes, each class is composed

characteristic attributes, we can use Euclidean distance between the two to express the dissimilarity degree. For different types of variables, the difference in the calculation method is not the same, here no longer repeat.The following is an introduction to the K-means Clustering algorithm, which is the simplest and simplest clustering algorithm, and the process is as follows:1, the K points are randomly selected as the centers of the K clusters ac