Disadvantages of k-means. As increases, you need advanced versions of k-means to pick better values of the initial centroids (called k-means seeding). For a full discussion of k- means seeding see, A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm by M.

Since the algorithm iterates a function whose domain is a finite set, the iteration must eventually enter a cycle. Hence k-means converges in a finite number of iterations.

Show that K-means is guaranteed to converge (to a local optimum). To prove convergence of the K-means algorithm, we show that the loss function is guaranteed to decrease monotonically in each iteration until convergence for the assignment step and for the refitting step.

K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique.

K Means Numerical Example. The basic step of k-means clustering is simple. In the beginning we determine number of cluster K and we assume the centroid or center of these clusters. Determine the distance of each object to the centroids. Group the object based on minimum distance.

The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. These centroids are used to train a kNN classifier. …

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets

The basic k-means clustering is based on a non-deterministic algorithm. This means that running the algorithm several times on the same data, could give different results. However, to ensure consistent results, FCS Express performs k-means clustering using a deterministic method.

K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning. K-Means performs division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster

The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It’s easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.

What Is Random Forest? Random forest is a supervised learning algorithm. The “forest” it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. It was initially introduced for text categorisation tasks and still is used as a benchmark

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data. Examples of deep structures that can be trained in an unsupervised manner are neural history compressors and deep belief networks.

Partitioning: This method requires only two database scans to mine the frequent itemsets. It says that for any itemset to be potentially frequent in the database, it should be frequent in at least one of the partitions of the database

Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Apriori is an influential algorithm that used in data mining. The name of the algorithm is based on the fact that the algorithm uses prior knowledge of frequent item set properties. The software is used for discovering the social status of the diabetics.

LIMITATIONS OF APRIORI ALGORITHM Apriori algorithm suffers from some weakness in spite of being clear and simple. The main limitation is costly wasting of time to hold a vast number of candidate sets with much frequent itemsets, low minimum support or large itemsets.

To improve the efficiency of level-wise generation of frequent itemsets, an important property is used called Apriori property which helps by reducing the search space. All subsets of a frequent itemset must be frequent(Apriori propertry). If an itemset is infrequent, all its supersets will be infrequent

The Minimum Support Count would be count of transactions, so it would be 60% of the total number of transactions. If the number of transactions is 5, your minimum support count would be 5*60/100 = 3

4 Answers. If the association rule is (2,5) -> (3), than is X = (2,5) and Y = (3). The confidence of an association rule is the support of (X U Y) divided by the support of X. Therefore, the confidence of the association rule is in this case the support of (2,5,3) divided by the support of (2,5)

Support represents the popularity of that product of all the product transactions. Confidence can be interpreted as the likelihood of purchasing both the products A and B. Confidence is calculated as the number of transactions that include both A and B divided by the number of transactions includes only product A

Given a set of transactions, we can find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction….

1. Support(s) –
2. Support = (X+Y) total –
3. Confidence(c) –
4. Conf(X=>Y) = Supp(X Y) Supp(X) –
5. Lift(l) –
6. Lift(X=>Y) = Conf(X=>Y) Supp(Y) –

Apriori uses two pruning technique, first on the bases of support count (should be greater than user specified support threshold) and second for an item set to be frequent , all its subset should be in last frequent item set The iterations begin with size 2 item sets and the size is incremented after each iteration.

The confidence of an association rule is a percentage value that shows how frequently the rule head occurs among all the groups containing the rule body. Thus, the confidence of a rule is the percentage equivalent of m/n, where the values are: m. The number of groups containing the joined rule head and rule body.

How to interpret the results? For an association rule X ==> Y, if the lift is equal to 1, it means that X and Y are independent. If the lift is higher than 1, it means that X and Y are positively correlated. If the lift is lower than 1, it means that X and Y are negatively correlated.