When the data mining procedures deals with the extraction of interesting knowledge from web logs is known as Web usage mining. The result of any mining is successful, only if the dataset under consideration is well preprocessed. One of the important preprocessing steps is handling of null/missing values. Handlings of null values have been a great bit of test for researcher. Various methods are ...

Graph Mining Under Linguistic Constraints to Explore Large Texts In this paper, we propose an approach to explore large texts by highlighting coherent sub-parts. The exploration method relies on a graph representation of the text according to the Hoey linguistic model which allows the selection and the binding of sentences in the graph. Our contribution relates to using graph mining techniques ...

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and funct...

We present a general approach to speeding up afamily of multi-relational data mining algorithmsthat construct and use selection graphs to obtain theinformation needed for building predictive mod-els (e.g., decision tree classifiers) from relationaldatabase. Preliminary results of our experimentssuggest that the proposed method can yield 1-2 or-ders of magnitude reduc...

Reinforcement evolutionary learning using data mining algorithm (R-ELDMA) with a TSK-type fuzzy controller (TFC) for solving reinforcement control problems is proposed in this study. R-ELDMA aims to determine suitable rules in a TFC and identify suitable and unsuitable groups for chromosome selection. To this end, the proposed R-ELDMA entails both structure and parameter learning. In structure ...

Heterogeneous graph based information recommendation have been proved useful in recent studies. Given a heterogeneous graph scheme, there are many possible meta paths between the query node and the result node, and each meta path addresses a hypothesis-based ranking function. In prior researches, meta paths are manually selected by domain experts. However, when the graph scheme becomes complex,...

Data Mining is characterised by its ability at processing large amounts of data. Among those are the data ”features”variables or association rules that can be derived from them. Selecting the most interesting features is a classical data mining problem. That selection requires a large number of tests from which arise a number of false discoveries. An original non parametric control method is pr...

Similarity measure is the base task of time series data mining tasks. LCSS measure method has obvious limitations in the two different length time series selection of a linear function. The ELCS measure method is proposed to normalize the sequence, which introducing the scale factor to limit the search path of the similarity matrix. Experiment in hierarchical clustering algorithm shows that the...

Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multiobjective problem rather than a single objective one. As the problem is a discrete combinator...

Opinion mining, a sub-discipline of information retrieval and computational linguistics concerns not with what a document is about, but with its expressed opinion. Feature selection is an important step in opinion mining, as customers express product opinions separately according to individual features. Earlier research on feature-based opinion mining had many drawbacks like selecting a feature...