## Data mining and text mining polimi

The Jaccard Index (Halkidi et al., ), also known as the Jaccard similarity coefficient is defined by () J (P ∗, P) = ∑ i, j (N i j 2) ∑ i (N i 2) + ∑ j (N j 2) − ∑ i (N i j 2). 22/03/ · Cheng J., Zhang L. () Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity. In: Yang Q., Zhou ZH., Gong Z., Zhang ML., Huang SJ. (eds) Advances in Knowledge Discovery and Data bundestagger.de by: 6. Jaccard Similarity •The Jaccard similarity (Jaccard coefficient) of two sets S 1, S 2 is the size of their intersection divided by the size of their union. •JSim (S 1, S 2) = |S 1 S 2 | / |S 1 S 2 |. •Extreme behavior: • Jsim(X,Y) = 1, iff X = Y • Jsim(X,Y) = 0 iff X,Y have no elements in common •JSim is symmetric 3 in intersection. 8 in union. 24/05/ · The coefficient to jaccard coefficient example in data mining: a measurement in a number of. Find more computationally efficient and each item or responding to predict human use jaccard coefficient example in data mining is a constant image? Learning analytics and jaccard coefficient example in data mining tasks such algorithms like netflix and.

Cleansing of data for text mining and finding similarities between documents using Jacard and cosine similarities. And computed TF-IDF coefficeints. Use Git or checkout with SVN using the web URL. Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. There was a problem preparing your codespace, please try again.

Finding the closeness between documents using basics of NLP. Implementing basics of text similarity on multiple files and presenting the analysis. Jaccard Similarity : The Jaccard similarity index sometimes called the Jaccard similarity coefficient compares members for two sets to see which members are shared and which are distinct.

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The Jaccard Index is a useful measure of similarity between two sets. It makes sense for any two sets, is efficient to compute at scale and it’s arithmetic complement is a metric. However for clustering it has one major disadvantage; small sets are never close to large sets. Suppose you have sets that you want to cluster together for analysis. For example each set could be a website and the elements are people who visit that website.

We want to group together similar websites. There’s a niche blog B , and every single person who visits it visits a very popular news aggregator A. The Jaccard similarity is simply the number of people who visit website B divided by the number of people who visit site A , which is a very small number. However B will be quite similar to another niche blog C that a few of it’s members visit.

Depending on your application this might be the wrong metric; you really do want to emphasise that B is similar to A.

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This page is a quick overview of each of the similarity metrics we examined in the course. For algorithms we implemented, sample source code is provided. A simple – and slightly circular – definition of similarity is a numerical measure of the degree to which two data objects are alike. In general, two objects are similar if they share many categorical attributes, or if the values of their numerical attributes are relatively close.

Often, categorical attributes must be transformed into binary attributes indicating whether or not the data object belongs to that category. Regardless of the measure of similarity used, a high similarity score indicates that two data objects are closely related. The opposite of similarity is, unsurprisingly, dissimilarity, which is often referred to as the distance between two objects.

Minkowski distance refers to a family of measures related by the formula 2 :. To visualize the Euclidean distance similarity between two objects, imagine that each is plotted in an n-dimensional space. Their similarity is the straight line distance between the points. Here is a simple Python implementation of the Euclidean distance algorithm used to calculate the similarity between two people based on their ratings of movies.

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In this essay, we take a detailed look into a set-similarity measure called – Jaccard’s Similarity Coefficient and how its computation can be optimized using a neat technique called MinHash. Jaccard Similarity Coefficient quantifies how similar two finite sets really are and is defined as the size of their intersection divided by the size of their union. This similarity measure is very intuitive and we can clearly see that it is a real-valued measure bounded in the interval [0, 1].

The coefficient is 0 when the two sets are mutually exclusive disjoint and it is 1 when the sets are equal. Below we see the one-line python function that computes this similarity measure. Jaccard Coefficient can also be interpreted as the probability that an element picked at random from the universal set U is present in both sets A and B. Another analogy for this probability is the chances of throwing a dart and it hitting the intersection.

Thus we see how we can transform the Jaccard Similarity Coefficient into a simple probability statement. This will come in very handy when we try to optimize the computation at scale. Computing Jaccard Similarity Coefficient is very simple, all we require is a union operation and an intersection operation on the participating sets. But these computations go haywire when things run at scale. Computing set similarity is usually a subproblem fitting in a bigger picture, for example, near-duplicate detection which finds near-duplicate articles across millions of documents.

When we tokenize the documents and apply raw Jaccard Similarity Coefficient for every two combinations of documents we find that the computation will take years. Instead of finding the true value for this coefficient, we can rely on an approximation if we can get a considerable speedup and this is where a technique called MinHash fits well.

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Which of the following is defined as the ratio of total elements of intersection and union of two sets? A Rope Tree B Jaccard Coefficient Index C Tango Tree D MinHash Coefficient Explanation: MinHash is a tool for quickly estimating the similarity of two sets. The Jaccard Coefficient is a measure of how close two sets are. The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets.

What is the value of the Jaccard index when the two sets are disjoint? A 1 B 2 C 3 D 0 Explanation: MinHash is a tool for quickly estimating the similarity of two sets. The value of the Jaccard index is zero for two disjoint sets. When are the members of two sets more common relatively? A Jaccard Index is Closer to 1 B Jaccard Index is Closer to 0 C Jaccard Index is Closer to -1 D Jaccard Index is Farther to 1 Explanation: The Jaccard Coefficient Index is the ratio of total intersection and union elements of two sets.

When the Jaccard Index is closer to 1, members of two sets are more common. What is the expected error for estimating the Jaccard index using MinHash scheme for k different hash functions? How many hashes will be needed for calculating Jaccard index with an expected error less than or equal to 0. Calculating the Jaccard index with an estimated error of less than or equal to 0.

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Wenshuai Wu, Zeshui Xu, Gang Kou, Yong Shi, “ Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM „, Complexity , vol. In many disciplines, the evaluation of algorithms for processing massive data is a challenging research issue. However, different algorithms can produce different or even conflicting evaluation performance, and this phenomenon has not been fully investigated.

The motivation of this paper aims to propose a solution scheme for the evaluation of clustering algorithms to reconcile different or even conflicting evaluation performance. The goal of this research is to propose and develop a model, called decision-making s upport for evaluation of clustering algorithms DMSECA , to evaluate clustering algorithms by merging expert wisdom in order to reconcile differences in their evaluation performance for information fusion during a complex decision-making process.

The proposed model is tested and verified by an experimental study using six clustering algorithms, nine external measures, and four MCDM methods on 20 UCI data sets, including a total of 18, instances and attributes. The proposed model can generate a list of algorithm priorities to produce an optimal ranking scheme, which can satisfy the decision preferences of all the participants. The results indicate our developed model is an effective tool for selecting the most appropriate clustering algorithms for given data sets.

Furthermore, our proposed model can reconcile different or even conflicting evaluation performance to reach a group agreement in a complex decision-making environment. Clustering is widely applied in the initial stage of big data analysis to divide large data sets into smaller sections, so the data can be comprehended and mastered easily with successive analytic operations [ 1 — 3 ].

The processing of massive data relies on the selection of an appropriate clustering algorithm, and the issue of the evaluation of clustering algorithms remains an active and significant issue in many subjects, such as fuzzy set, genomics, data mining, computer science, machine learning, business intelligence, and financial analysis [ 1 , 4 — 6 ]. Computer scientists, economists, political scientists, bioinformatics specialists, sociologists, and many other groups usually debate the potential costs and benefits by analyzing these data for supporting decision-making [ 7 ].

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Please cite us if you use the software. Read more in the User Guide. The set of labels to include when average! Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices.

If None , the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:. Calculate metrics globally by counting the total true positives, false negatives and false positives. Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

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22/03/ · Cheng J., Zhang L. () Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity. In: Yang Q., Zhou ZH., Gong Z., Zhang ML., Huang SJ. (eds) Advances in Knowledge Discovery and Data Mining. The Jaccard coefficient is a similar method of comparison to the Cosine Similarity due to how both methods compare one type of attribute distributed among all data. The Jaccard approach looks at the two data sets and finds the incident where both values are equal to 1. So the resulting value reflects how many 1 to 1 matches occur in comparison.

Distance or similarity measures are essential to solve many pattern recognition problems such as classification and clustering. As the names suggest, a similarity measures how close two distributions are. For multivariate data complex summary methods are developed to answer this question. Distance , such as the Euclidean distance, is a dissimilarity measure and has some well known properties:.

A distance that satisfies these properties is called a metric. Following is a list of several common distance measures to compare multivariate data. We will assume that the attributes are all continuous. The Euclidean distance between the i th and j th objects is. If scales of the attributes differ substantially, standardization is necessary. The Minkowski distance is a generalization of the Euclidean distance.

Then the i th row of X is. Calculate the answers to these questions by yourself and then click the icon on the left to reveal the answer.