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Keywords: Data clustering Artificial bee colony Hybrid artificial bee colony Crossover operator a b s t r a c t Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony(More)
Nowadays, short texts are very prevalent in various web applications, such as microblogs, instant messages. The severe sparsity of short texts hinders existing topic models to learn reliable topics. In this paper, we propose a novel way to tackle this problem. The key idea is to learn topics by exploring term correlation data, rather than the(More)
Bursty topics discovery in microblogs is important for people to grasp essential and valuable information. However, the task is challenging since microblog posts are particularly short and noisy. This work develops a novel probabilistic model, namely Bursty Biterm Topic Model (BBTM), to deal with the task. BBTM extends the Biterm Topic Model (BTM) by(More)
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main(More)
Traditional Chinese medicine has gained popularity due to its ability to kill tumor cells. Recently, the apoptotic and anti-angiogenic effects of Trametes robiniophila murr (Huaier) have been investigated. The aim of this study was to investigate its effect on cell mobility and tumor growth in ovarian cancer. Cell viability and motility were measured using(More)
Epithelial-mesenchymal transition (EMT) has been recognized as a key element of cell migration, invasion, and drug resistance in several types of cancer. In this study, our aim was to clarify microRNAs (miRNAs)-related mechanisms underlying EMT followed by acquired resistance to chemotherapy in glioblastoma (GBM). We used multiple methods to achieve our(More)
Non-negative matrix factorization (NMF) has been successfully applied in document clustering. However, experiments on short texts, such as microblogs, Q&#38;A documents and news titles, suggest unsatisfactory performance of NMF. An major reason is that the traditional term weighting schemes, like binary weight and <i>tfidf</i>, cannot well capture the(More)
Query recommendation has been widely used in modern search engines. Recently, several context-aware methods have been proposed to improve the accuracy of recommendation by mining query sequence patterns from query sessions. However, the existing methods usually do not address the ambiguity of queries explicitly and often suffer from the sparsity of the(More)