By Joao Gama
Since the start of the web age and the elevated use of ubiquitous computing units, the big quantity and non-stop stream of disbursed information have imposed new constraints at the layout of studying algorithms. Exploring the best way to extract wisdom buildings from evolving and time-changing info, Knowledge Discovery from info Streams offers a coherent review of cutting-edge study in studying from information streams.
The booklet covers the basics which are important to knowing information streams and describes vital functions, comparable to TCP/IP site visitors, GPS info, sensor networks, and purchaser click on streams. It additionally addresses numerous demanding situations of knowledge mining sooner or later, whilst move mining can be on the middle of many purposes. those demanding situations contain designing valuable and effective information mining ideas appropriate to real-world difficulties. within the appendix, the writer contains examples of publicly to be had software program and on-line facts sets.
This sensible, up to date booklet makes a speciality of the recent requisites of the subsequent iteration of information mining. even supposing the ideas offered within the textual content are frequently approximately info streams, in addition they are legitimate for various parts of laptop studying and information mining.
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Extra info for Knowledge Discovery from Data Streams
Learning algorithms that model the underlying processes must be able to track this behavior and adapt the decision models accordingly. 2 Tracking Drifting Concepts Concept drift means that the concept about which data is being collected may shift from time to time, each time after some minimum permanence. Changes occur over time. The evidence of drift in a concept is reflected in some way in the training examples. Old observations, which reflect the behavior of nature in the past, become irrelevant to the current state of the phenomena under observation and the learning agent must forget that information.
The actual size of the data warehouse is 3 TB of data, and hundreds of gigabytes of new sales records are updated daily. The order of magnitude of the different items is millions. The hot-list problem consists of identifying the most (say 20) popular items. Moreover, we have restricted memory: we can have a memory of hundreds of bytes only. The goal is to continuously maintain a list of the top-k most frequent elements in a stream. Here, the goal is the rank of the items. The absolute value of counts is not relevant, but their relative position.
The size of the window is defined in terms of duration. A timestamp window of size t consists of all elements whose timestamp is within a time interval t of the current time period. Computing statistics over sliding windows requires storing all elements inside the window in memory. Suppose we want to maintain the standard deviation of the values of a data stream using only the last 100 examples, that is, in a fixed time window of dimension 100. After seeing observation 1000, the observations inside the time window are: x901 , x902 , x903 , .