The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

Abstract

In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users' interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R=(U,A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ=(αa:a∈A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k-means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.

Time series classification based on statistical features

“…Several problems remain unsolved. Some of the interesting problems are how to adjust subsequence length/ step length and regularize a network and how to use LSTM-FCN, ALSTM-FCN [9], hierarchies of feature [19], or time aware [20,21] to improve the performance of time series classification. In addition, we will try to apply the method of classification service in distributed cloud/fog environment [22][23][24][25][26][27][28][29][30][31][32] in the future.…”