I'm looking for some planning algorithms that i can use to collect informations from sensor nodes in WSN. I found Dijkstra Algorithm but the problem with it is that it can't go through all the given nodes. I need 3 algorithms for my test.
Related
I have been experimenting with Lumer-Faieta clustering and I am getting
promising results:
However, as clusters formed I was wondering how to identify the final clusters? Do I run another clustering algorithm to identify the clusters (that seems counter-productive)?
I had the idea of starting each data point in its own cluster. Then, when a laden ant drops a data point, its gets the same cluster as the data points that dominates its neighborhood. The problem with this is that if clusters are broken up, they share share the same cluster number.
I am stuck. Any suggestions?
To solve this problem, I employed DBSCAN as a post processing step. The effect as follows:
Given that we have a projection of a high dimensional problem on a 2D grid, with known distances and uniform densities, DBSCAN is ideal for this problem. Choosing the right value for epsilon and the minimum number of neighbours are trivial (I used 3 for both values). Once the clusters have been identified, it can be projected back to the n-dimension space.
See The 5 Clustering Algorithms Data Scientists Need to Know for a quick overview (and graphic demo) of DBSCAN and some other clustering algorithms.
I am working on instances from the TSPLIB, which are simply coordinates of nodes in a plan. I'm looking to analyze spatial characteristics and features of a set of instances (e.g. clustered, not clustered, dispersed, etc) and I would like to implement some code in Matlab to analyze and compute specific features.
For example, so far, I have used Nearest Neighbor analysis to identify clusters, as well as quadrant analysis. Can anyone suggest any other spatial features and patterns that could be computed with some relatively simple code? Anybody maybe expert in the Traveling Salesman Problem. Thank you so much!
K-means is a very useful clustering tool that you can use.
https://www.mathworks.com/help/stats/kmeans.html
Nearest Neighbor is a classification methods. if you want to do classification you can use K Nearest Neighbors, SVM or Neural Networks Pattern recognition toolbox. these are all already in Matlab.
Also, check out Matlab Apps. there are some very cool clustering tools available as well with examples.
Context: We are two students intending to write a thesis on reverse engineering namespaces using hierarchical agglomerative clustering algorithms. We have a variation of linking methods and other tweaks to the algorithm we want to try out. We will run the algorithm on popular GitHub repositories and compare the created clusters with the originally existent namespaces. Our work will closely follow the works of this paper. In the paper the authors mentions the use of the “precision recall metric” to measure the accuracy of the clustering algorithm. However looking more closely on the metric and its origin, it seems to be dedicated to flat (non-hierarchical) clusters.
Question:
Is there a way to use the precision recall metric to measure the accuracy of a hierarchy of recovered clusters? If not, what other options exists?
I have a data set which consists of data points having attributes like:
average daily consumption of energy
average daily generation of energy
type of energy source
average daily energy fed in to grid
daily energy tariff
I am new to clustering techniques.
So my question is which clustering algorithm will be best for such kind of data to form clusters ?
I think hierarchical clustering is a good choice. Have a look here Clustering Algorithms
The more simple way to do clustering is by kmeans algorithm. If all of your attributes are numerical, then this is the easiest way of doing the clustering. Even if they are not, you would have to find a distance measure for caterogical or nominal attributes, but still kmeans is a good choice. Kmeans is a partitional clustering algorithm... i wouldn't use hierarchical clustering for this case. But that also depends on what you want to do. you need to evaluate if you want to find clusters within clusters or they all have to be totally apart from each other and not included on each other.
Take care.
1) First, try with k-means. If that fulfills your demand that's it. Play with different number of clusters (controlled by parameter k). There are a number of implementations of k-means and you can implement your own version if you have good programming skills.
K-means generally works well if data looks like a circular/spherical shape. This means that there is some Gaussianity in the data (data comes from a Gaussian distribution).
2) if k-means doesn't fulfill your expectations, it is time to read and think more. Then I suggest reading a good survey paper. the most common techniques are implemented in several programming languages and data mining frameworks, many of them are free to download and use.
3) if applying state-of-the-art clustering techniques is not enough, it is time to design a new technique. Then you can think by yourself or associate with a machine learning expert.
Since most of your data is continuous, and it reasonable to assume that energy consumption and generation are normally distributed, I would use statistical methods for clustering.
Such as:
Gaussian Mixture Models
Bayesian Hierarchical Clustering
The advantage of these methods over metric-based clustering algorithms (e.g. k-means) is that we can take advantage of the fact that we are dealing with averages, and we can make assumptions on the distributions from which those average were calculated.
I was going through the K-means algorithm in mahout and when debugging, I noticed that when creating the first clusters it does this following code:
ClusteringPolicy policy = new KMeansClusteringPolicy(convergenceDelta);
ClusterClassifier prior = new ClusterClassifier(clusters, policy);
prior.writeToSeqFiles(priorClustersPath);
I was reading the description of these classes and it was not clear for me...
I was wondering what is the meaning of these cluster classifier and policy?
is it related with hierarchical clustering, centroid based clustering, distribution based
clustering etc?
Because I do not know what is the benefit or the reason of using this cluster classifier and policy when using K-means mahout implementation.
The implementation shares code with other variants of k-means and similar algorithms such as Canopy pre-clustering and GMM.
These classes encode only the difference between these algorithms.
Mahout is not a good place to study the k-means algorithm, the implementation is quite a mess. It's also slow. As in really really slow. Most of the time, a single CPU implementation will outright beat Mahout on anything that fits into memory. Maybe even on disk of a single machine. Because of all the map-reduce overhead.