... gender, age, product, etc.) into numeric values so can be
treated as points in space
• If two points are close in geometric sense then they
represent similar data in the database
Introduction to Clustering
... Cluster is a collection of data objects that are similar to one another within the same cluster
and are dissimilar to the objects in other cluster.
Cluster: a collection of data objects
The goal of data mining is to extract knowledge, dependencies and
... networks with the backpropagation algorithm, RBF networks, Kohonens maps and some modifications
of LVQ method. There are also described some clustering methods like hierarchical clustering, QT
clustering, kmeans method and its fuzzy modification. The work also includes data pre-processing
A New Gravitational Clustering Algorithm
... Many clustering techniques rely on the assumption that a
data set follows a certain distribution and is free of noise
Given noise, several techniques (k-means, fuzzy k-means)
based on a least squares estimate are spoiled
Most clustering algorithms require the number of clusters
to be specified
The a ...
... Select initial centroids at random.
Assign each object to the cluster with the
Compute each centroid as the mean of the
objects assigned to it.
Repeat previous 2 steps until no change.
Data Mining Lab
... 2. Pre-process a given dataset based on the following:
a. Attribute Selection
b. Handling Missing Values
d. Eliminating Outliers
3. Create a dataset in ARFF (Attribute-Relation File Format) for any given dataset and perform
4. Generate Association Rules usin ...
Clustering in Data Mining ( Phuong Tran)
... Some elements may be close according to
one distance measure and further away
according to another.
Select a good distance measure is an
important step in clustering.
... respect to Q (theta fixed) and then maximizing F with
respect to theta (Q fixed).
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 to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.The algorithm has a loose relationship to the k-nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k-means because of the k in the name. One can apply the 1-nearest neighbor classifier on the cluster centers obtained by k-means to classify new data into the existing clusters. This is known as nearest centroid classifier or Rocchio algorithm.