There are some variations of the Apriori algorithm that have been projected that target developing the efficiency of the original algorithm which are as follows −The hash-based technique (hashing itemsets into corresponding buckets) − A hash-based technique can be used to decrease the size of the candidate k-itemsets, Ck, for k > 1. For instance, when scanning each transaction in the database to create the frequent 1-itemsets, L1, from the candidate 1-itemsets in C1, it can make some 2-itemsets for each transaction, hash (i.e., map) them into the several buckets of a hash table structure, and increase the equivalent bucket ... Read More

There are the various web-based tools which are as follows −Arbor Essbase Web − This tool provides features as drilling up, down, across; slice and dice, and powerful reporting, all for OLAP. It also provides data entry, such as full multi-user concurrent write capabilities. Arbor Essbase is only a server product, no user package exists, thus assuring its own desktop client version market. The Web product does not restore administrative and development structures but it restores only user access for queries and updates.Information Advantage Web OLAP − This product uses a server-centric messaging architecture, composed of a powerful analytic engine ... Read More

The FASMI TestIt can represent the characteristics of an OLAP application in a specific method, without dictating how it should be performed.Fast − It defines that the system is targeted to produce most responses to users within about five seconds, with the understandable analysis taking no more than one second and very few taking more than 20 seconds.Independent research in the Netherlands has shown that end-users consider that a process has declined if results are not received with 30 seconds, and they are suitable to hit ‘ALT+Ctrl+Delete’ unless the system needs them that the report will take longer.Analysis − It ... Read More

A hierarchical clustering technique works by combining data objects into a tree of clusters. Hierarchical clustering algorithms are either top-down or bottom-up. The quality of an authentic hierarchical clustering method deteriorates from its inability to implement adjustment once a merge or split decision is completed.The merging of clusters is based on the distance among clusters. The broadly used measures for the distance between clusters are as follows, where mi is the mean for cluster Ci, ni is the number of points in Ci, and |p – p’| is the distance among two points p and p'.Types of Hierarchical Clustering MethodsThere ... Read More

A statistical discordancy test analysis two hypotheses; a working hypothesis and a different hypothesis. A working hypothesis, H, is a statement that the entire data set of n objects comes from an initial distribution model, F, i.e., H: oi Î F, where i = 1, 2, n.The hypothesis is retained if there is no statistically important evidence supporting its rejection. A discordancy test checks whether an object oi is essentially large (or small) regarding the distribution F. Different test statistics have been proposed for use as a discordancy test, based on the available knowledge of the data.Suppose that some statistic ... Read More

There are various methods of clustering which are as follows −Partitioning Methods − Given a database of n objects or data tuples, a partitioning method assembles k partitions of the information, where each partition defines a cluster, and k < n. It can allocate the data into k groups, which can satisfy the following necessity −Each group must include a minimum of one object.Each object should apply to accurately one group.Given k, the number of partitions to construct, a partitioning method makes an initial partitioning. It then uses an iterative relocation method which attempts to improve the partitioning by transforming ... Read More

There are various applications of clustering which are as follows −Scalability − Some clustering algorithms work well in small data sets including less than 200 data objects; however, a huge database can include millions of objects. Clustering on a sample of a given huge data set can lead to biased results. There are highly scalable clustering algorithms are required.Ability to deal with different types of attributes − Some algorithms are designed to cluster interval-based (numerical) records. However, applications can require clustering several types of data, including binary, categorical (nominal), and ordinal data, or a combination of these data types.Discovery of ... Read More

There are various challenges of data mining which are as follows −Efficiency and scalability of data mining algorithms − It can effectively extract data from a large amount of data in databases, the knowledge discovery algorithms should be efficient and scalable to huge databases. Specifically, the running time of a data mining algorithm should be predictable and acceptable in huge databases. Algorithms with exponential or even channel-order polynomial complexity will not be of efficient use.Usefulness, certainty, and expressiveness of data mining results − The identified knowledge should exactly portray the contents of the database and be beneficial for specific applications. ... Read More

Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.Data mining systems are designed to promote the identification and classification of individuals into different groups or segments. From the aspect of the commercial firm, and possibly for the industry as a whole, it can interpret the use of ... Read More

Data mining is one of the forms of artificial intelligence that uses perception models, analytical models, and multiple algorithms to simulate the techniques of the human brain. Data mining supports machines to take human decisions and create human choices.The user of the data mining tools will have to direct the machine rules, preferences, and even experiences to have decision support data mining metrics are as follows −Usefulness − Usefulness involves several metrics that tell us whether the model provides useful data. For instance, a data mining model that correlates save the location with sales can be both accurate and reliable, ... Read More