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There are several tasks of link mining which are as follows −Link-based object classification − In traditional classification approaches, objects are classified depending on the attributes that define them. Link-based classification predicts the category of an object depends not only on its attributes, but also on its links, and the attributes of linked objects.Web page classification is a well-identified instance of link-based classification. It predicts the classification of a web page based on word appearance (words that appear on the page) and anchor text (the hyperlink words, that is, the words it can click on when it can click on ... Read More
A social network is a heterogeneous and multi-relational information set described by a graph. The graph is generally very large, with nodes corresponding to objects and edges corresponding to connections describing relationships or connections between objects. Both nodes and connections have attributes. Objects can have class labels. Links can be one-directional and are not needed to be binary.A social network is a heterogeneous and multi-relational information set described by a graph. The graph is generally very large, with nodes corresponding to objects and edges corresponding to connections describing relationships or connections between objects. Both nodes and connections have attributes. Objects ... Read More
The discovery of frequent substructures usually consists of two steps. In the first step, it can make frequent substructure candidates. The frequency of every candidate is tested in the second step. Most studies on frequent substructure discovery focus on the optimization of the first step because the second step involves a subgraph isomorphism test whose computational complexity is excessively high (i.e., NP-complete).There are various methods for frequent substructure mining which are as follows −Apriori-based Approach − Apriori-based frequent substructure mining algorithms send the same features with Apriori-based frequent itemset mining algorithms. The search for frequent graphs begins with graphs of ... Read More
Periodicity analysis is the mining of periodic patterns, namely, the search for recurring patterns in time-related series data. Periodicity analysis can be used in several important areas. For example, seasons, tides, planet trajectories, daily power consumptions, daily traffic patterns, and weekly TV programs all present certain periodic patterns.Periodicity analysis is implemented over time-series data, which includes sequences of values or events generally measured at equal time intervals (e.g., hourly, daily, weekly). It can also be applied to other time-related sequence data where the value or event may occur at a non-equal time interval or at any time (e.g., online transactions). ... Read More
A time-series database includes sequences of values or events accessed over the repeated assessment of time. The values are generally calculated at equal time intervals (e.g., hourly, daily, weekly). Time-series databases are popular in many applications, such as stock market analysis, economic and sales forecasting, budgetary analysis, utility studies, inventory studies, yield projections, workload projections, process and quality control, observation of natural phenomena (including atmosphere, temperature, wind, and earthquake), numerical and engineering experiments, and medical treatments.A time-series database is also a sequence database. A sequence database is any database that includes sequences of ordered events, with or without a concrete ... Read More
To solve a problem in which we are given an array containing the size of the boxes. Now we are given a condition that we can fit a smaller box inside a bigger box if the bigger box is at least twice the size of the smaller box. Now we must determine how many visible boxes there are, for example.Input : arr[] = { 1, 3, 4, 5 } Output : 3 Put a box of size 1 in the box of size 3. Input : arr[] = { 4, 2, 1, 8 } Output : 1Approach to Find ... Read More
CluStream is an algorithm for the clustering of evolving data streams based on userspecified, online clustering queries. It divides the clustering process into on-line and offline components.The online component computes and stores summary statistics about the data stream using micro-clusters, and performs incremental online computation and maintenance of the micro-clusters. The offline component does macro-clustering and answers various user questions using the stored summary statistics, which are based on the tilted time frame model.The cluster evolving data streams based on both historical and current stream data information, the tilted time frame model (such as a progressive logarithmic model) is adopted, ... Read More
The Hoeffding tree algorithm is a decision tree learning method for stream data classification. It was initially used to track Web clickstreams and construct models to predict which Web hosts and Web sites a user is likely to access. It typically runs in sublinear time and produces a nearly identical decision tree to that of traditional batch learners.It uses Hoeffding trees, which exploit the idea that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound (or additive Chernoff bound).Suppose we make N independent observations of a random ... Read More
BIRCH represents Balanced Iterative Reducing and Clustering Using Hierarchies. It is designed for clustering a huge amount of numerical records by integration of hierarchical clustering and other clustering methods including iterative partitioning.BIRCH offers two concepts, clustering feature and clustering feature tree (CF tree), which are used to summarize cluster description. These structures facilitate the clustering method to achieve the best speed and scalability in huge databases and also create it effective for incremental and dynamic clustering of incoming objects.Given n d-dimensional data objects or points in a cluster, and it can represent the centroid x0, radius R, and diameter D ... Read More
An object o in a data set S is a distance-based (DB) outlier with parameters p and d, i.e., DB (p, d), if minimum a fraction p of the objects in S lie at a distance higher than d from o. In other words, instead of depending on statistical tests, it can think of distance-based outliers as those objects who do not have enough neighbors.The neighbors are represented based on distance from the given object. In comparison with statistical-based methods, distance-based outlier detection generalizes or merges the ideas behind discordancy testing for standard distributions. Hence, a distance-based outlier is also ... Read More